TopicIssueVirtual Topic
Top ViewTop Cited
- Big Data GIS
- Probability Estimation of Future Earthquakes in China Based on Improved PSHA Model
- Experimental Geography Based on Virtual GeographicEnvironments(VGEs)
- Big Data in Smart City
- An Improved Cellular Automata Model for Simulating Pedestrian Evacuation
- Self-adaptive Window Extension Strategies of Discontinuity Features for MVLL Algorithm
- Web GIS Component
- Introduction to Urban Computing
- Detecting “Hot Spots” of Facility POIs Based on Kernel Density Estimation and Spatial Autocorrelation Technique
- On Construction of China’s Space Information Network
- Integrated Space-Air-Ground Early Detection, Monitoring and Warning System for Potential Catastrophic Geohazards
- The Spatial-Temporal Pattern Analysis of City Development in Countries along the Belt and Road Initiative Based on Nighttime Light Data
- Oblique Image Based Automatic Aerotriangulation and Its Application in 3D City Model Reconstruction
Download CenterMore >
LinkMore >
Display Method:
2021, 46(12): 1791-1801.
doi: 10.13203/j.whugis20210474
Abstract:
Objectives With the increasing demand for location-based service(LBS) applications and the wide popularity of multi-mounted micro electro mechanical system(MEMS) navigation sensors, pedestrian dead reckoning (PDR) algorithm has attracted more and more attention and has become one of the mainstream algorithms in pedestrian navigation research. However, the low-cost MEMS sensor has high measurement noise and serious accumulation of PDR solution error. Moreover, the universality of PDR algorithm is poor, and the availability of constraints of MEMS navigation sensors with different wearing statte is significantly different. Methods A multi-mounted PDR algorithm based on wearable MEMS sensor state recognition is proposed. Firstly, support vector machine is used for fully supervised training to realize the accurate recognition of five wearing modes (hand, leg, waist, foot and stationary state). Then the applicability of PDR algorithm in different wearing states is analyzed, and a comprehensive solution strategy of multi position PDR is proposed based on the applicability analysis results. Results The measured results show that the wear recognition accuracy of MEMS sensor is more than 97%. The foot PDR can achieve high-precision solution, and the cumulative error is 0.74%, while the solution effect of other positions (hand, leg and waist) has been significantly improved, and the cumulative error has been reduced from 6.76%—21.19% before recognition to 2.92%—5.62% after recognition. Conclusions Therefore, the proposed algorithm can dynamically and accurately realize the state recognition of wearable MEMS sensors. After applying the PDR comprehensive solution strategy, the solution accuracy of PDR is significantly improved.
2021, 46(12): 1802-1807.
doi: 10.13203/j.whugis20210478
Abstract:
Objectives The current railway maintenance method is unitary and simple, which is a human force intensive work, requiring tedious and interminable manual check from professional technicians. As a multi-scale, multi-probability and long-term digital representation approach, the digital-twin has seen a rising popularity in engineering construction. Inspired by the convenience of digital-twin, this paper designs a simultaneous localization and mapping (SLAM) system for railway locomotives. Methods Unlike traditional mobile mapping system which demands high precision three-dimensional laser scanning, inertial measurement unit (IMU) / real time kinematic (RTK), as well as complicated post-processing methods, our solution is capable of real time mapping and odometry visualization through constructed factor graph. The factor graph is a bipartite graph with two node types: Factor nodes and variables nodes, and they are always connected by edges. A new variable node is added to the graph when the pose displacements exceed a certain threshold, then the factor graph is optimized upon the insertion. We use three types of factors for graph construction: IMU preintegration factors, light detection and ranging (LiDAR) odometry factors, RTK factors. Results and Conclusions The real time performance is further achieved through sliding window optimization. Under more than 300 km field test in various environments, our approach can achieve centimeter-lever positioning accuracy in feature rich and low speed cases, and the structures are clearly visible in the real time mapping result.
2021, 46(12): 1808-1818.
doi: 10.13203/j.whugis20210310
Abstract:
Objectives With the rise of location service applications and the popularization of mobile smart terminals, the demand for high-precision navigation and positioning is extending from isolated areas to seamless whole areas, from professional groups to mass users. However, real-time, continuous, and complete pedestrian navigation still faces many challenges, such as occlusion of global navigation satellite system (GNSS) signals, frequent updates of Wi-Fi/ bluetooth low energy (BLE)/geomagnetic fingerprint databases, and smoothly switching in the transitional area of indoor and outdoor. In addition, the positioning sensors of popular mobile smart terminals are limited by cost and power consumption, and the observation data is generally noisy and poor in stability. Methods Therefore, this paper proposes a pedestrian indoor and outdoor seamless positioning technology based on cloud-end collaboration of smartphone, which breaks through the crowd sourced collection and rapid update of the beacon fingerprint database, the precise positioning of the smart phone multi-sensor information fusion, and the indoor and outdoor seamless switching. We have developed a smartphone positioning application for pedestrians. The collaborative precision positioning enhancement information is provided by the location service platform and the multi-source observations data (GNSS, miniature inertial measurement unit (MIMU), Wi-Fi, BLE, magnetometer, barometer) are obtained by the smartphone. Results The feild test results show that the outdoor positioning accuracy of ordinary users is better than 1.5 m, and indoor accuracy is about 1‒3 m. Conclusions It supports thedemand of mass-market users for seamless and precise positioning indoors and outdoors.
2021, 46(12): 1819-1831.
doi: 10.13203/j.whugis20210469
Abstract:
Objectives In recent years, the brain-like navigation algorithm is a new research hotspot, which is expected to achieve autonomous navigation by imitating the ability of biological navigation. The core issue is how to improve generalization ability. Methods This paper introduces the research background and theoretical basis of the brain-like navigation algorithm. After investigation, we propose a computational framework of brain-like navigation algorithm. The outstanding works in this field are discussed and analyzed under this framework, and we carried out experimental verification of some basic methods. Results The main contributions of this paper are: (1) Comprehensively introduces and summarizes the theoretical basis and outstanding works in this field. (2) Proposes the computational framework of the brain-like navigation algorithm, which scientifically defines the functions of different modules of the algorithm. (3) Through theoretical analysis and experimental verification, we summarized valuable conclusions and expectations. Conclusions In terms of model design, mature methods of deep learning can also be applied to this problem, but need more modifications to further improve navigation capabilities; in terms of model training, combining the advantages of multiple learning algorithms is hopeful to further improve the generalization ability.
2021, 46(12): 1832-1842.
doi: 10.13203/j.whugis20210243
Abstract:
Objectives Slow convergence has always been an important factor of limiting the development of precise point positioning (PPP). Studies have shown that the convergence speed of PPP can be significantly improved with high-precision ionospheric delay correction, and then to achieve PPP-real time kinematic (PPP-RTK). At present, the ionosphere in the regional PPP-RTK mainly adopts the satellite-based ionospheric model with polynomial function (SIM_POLY) and the satellite-based ionospheric model with inverse distance weight function (SIM_IDW) for construction. Methods In order to verify the modeling accuracy of the above two models at different latitudes, this paper first used the observation data of Guangdong, Hubei and Hebei provinces to establish ionospheric delay models, and then applied the above models to float and fixed solutions PPP under single GPS and GPS+BDS systems. Finally the results were compared with IFPPP (ionosphere-free PPP) and CODE GIM (Centre for Orbit Determination in Europe Global Ionospheric Map, CODG) correction. Results Experimental results showed that in the low-latitude provinces, SIM_IDW model slightly outperformed SIM_POLY model, but there was no significant difference in the middle and high latitude provinces. Compared with IFPPP and CODG correction, PPP under SIM_IDW and SIM_POLY correction has a better performance. Besides, Hebei province achieved the fastest convergence speed compared with Guangdong and Hubei provinces, and compared with IFPPP, the positioning accuracy in single GPS solution under SIM_IDW and SIM_POLY model correction is improved by 43.7% and 43.0%, respectively. In the fixed PPP, the success rate of first epoch to fix the ambiguity of PPP-RTK under the correction of SIM_IDW and SIM_POLY models in GPS+BDS solution could reach 86.09% and 89.13% in Hebei province. Besides, positioning accuracy could converge to 5 cm in the first epoch in horizontal direction, and converge to 10 m within 1.5 minutes in vertical direction. The positioning accuracy after convergence of GPS+BDS PPP-RTK had a significant improvement compared with single GPS, which was 1.3 cm in horizontal direction and 3.5 cm in three-dimensional direction under the correction of SIM_IDW and SIM_POLY models. Conclusions Establishing a regional ionospheric model through SIM_IDW and SIM_POLY models to realize PPP-RTK can significantly shorten the PPP convergence time and improve positioning accuracy.
2021, 46(12): 1843-1851.
doi: 10.13203/j.whugis20210449
Abstract:
Objectives Sea ice concentration (SIC), which is defined as the proportion of a given area of ocean that is covered by ice, is a significant parameter for Arctic sea ice and climate change, an important input for regional climate model and numerical weather prediction model. The passive microwave sensors are able to penetrate the atmosphere and clouds, regardless of observation time, and their data is commonly used to retrieve SIC. Passive microwave obtained from the microwave radiation imager (MWRI) aboard on the Chinese Fengyun-3 (FY-3) series satellites can be used to extract polar SIC. However, the study on its accuracy is limited. Methods This paper focuses on the application of MWRI to Arctic SIC estimation accuracy, and compares the SIC obtained using special sensor microwave imager sounder (SSMIS), MWRI and advanced microwave scanning radiometer 2 (AMSR2) data, and utilizes Bootstrap, NASA Team(NT), fully constrained least squares (FCLS), Enhanced NASA Team (NT2) and Arctic radiation and turbulence interaction study (ARTIST) sea ice (ASI) methods with the in-situ data. Results Validation results show that MWRI has the smallest root mean square error (RMSE) at different spatial resolution (20.4%-24.4%). FCLS performed better than the other five algorithms for three passive microwave data because the error was considered during the SIC retrieve, the SIC was constrained with non-negative constrain, and numerical optimization was used to solved the SICs. In addition, MWRI performs well in summer (17.9%-23.0%) and winter (11.2%-17.8%). Conclusions MWRI has relatively stable performance and a great potential for sea ice monitoring. Uncertainty also exists in evaluation, such as the uncertainty of accuracy of in-situ data in Arctic and tie points. Furthermore, scale difference between multi-source data could also induce the uncertainty of validation.
2021, 46(12): 1852-1861.
doi: 10.13203/j.whugis20210615
Abstract:
Objectives PPP-RTK (precise point positioning real-time kinematic) is an effective positioning method, for it eliminates the limitations of regional service coverage in RTK(real-time kinematic) and long convergence time in PPP(precise point positioning). However, it is difficult for PPP-RTK to provide continuous and precise positioning results in urban areas for the signals are blocked. Inertial navigation system (INS) and visual navigation can provide continuous navigation information but suffers from error drifts.So, we proposed semi-tightly coupled multi-GNSS (global navigation satellite system) PPP-RTK/VIO (vision inertial odometry) model to complement each other. Methods In order to evaluate the performance of the integration model, firstly, we collected data in the campus of Wuhan University. Secondly, we used the data to test the integration model and compared the result with other solutions. Results The result shows that semi-tightly coupled multi-GNSS PPP-RTK/VIO can improve the positioning accuracy by more than 30% in comparison to GPS (global positioning system) +BDS (BeiDou navigation satellite system) PPP-RTK. Its accuracy is 0.58 m and 1.12 m in horizontal and vertical direction, respectively. Moreover, it also performs well in velocity and pose estimation. The accuracy of velocity estimation is 0.04 m/s, 0.04 m/s and 0.02 m/s, respectively, and the accuracy of pose estimation are 0.10 Conclusions Semi-tightly coupled multi-GNSS PPP-RTK/VIO can make use of their own advantages, improve the positioning performance of PPP-RTK and realize better velocity and pose estimation.
2021, 46(12): 1862-1871.
doi: 10.13203/j.whugis20210409
Abstract:
Objectives Existing deep reinforcement learning (DRL)based end-to-end autonomous driving decision-making method is low robustness to noise, which would lead to safety problem. It is difficult to infer the optimal decision accurately by relying solely on the image features when facing with the complex scenes. Methods An end-to-end decision-making model based on dueling deep Q-network(Dueling DQN) is established to improve the ability of decision evaluation and improve the robustness of the model. It obtains the current state according to the observed data, and outputs discrete quantities for controlling the vehicle (including throttle, steering and brake). The monocular depth feature is extracted accurately in a self-supervised learning manner, and which is combined with the image features for better representation of the current state. Results The proposed method is tested in a simulation environment. (1) The comparison results with the state-of-the-art A3C model show that our Dueling DQN-based model is more robustness. (2) The comparison results with the image feature-based model show that combining the image and depth features is more beneficial to improve the decision-making accuracy. Conclusions Training an agent with Dueling DQN is beneficial to alleviate the security risks caused by making different decision when facing similar scenes. Training an agent together with image features and depth features is beneficial to enhance the agent̓s ability of environment perception, and improve the decision-making accuracy.
2021, 46(12): 1872-1880.
doi: 10.13203/j.whugis20210249
Abstract:
Objectives With the popularity of mobile devices and low-cost sensors, the universal positioning capability of mobile terminals has become a strong demand. In order to build a universally available cloud positioning system, it is necessary to use various positioning sensors in smart terminals to obtain wireless signals, and search in the positioning database to complete the calculation of the position. Methods In light of smart phone devices in location based service(LBS) scenarios, we focus on location service for massive smart phone devices and put forward the cloud computing network architecture. The breakthroughs on key engineering technologies such as hierarchical cloud positioning capabilities, low-cost access for developers, fault tolerance strategy and distributed positioning services are gained. Results The testing result shows that cloud positioning system has realized the access processing capability of hundreds of billions of requests per day. Conclusions Consequently, the system can provide location services for hundreds of millions of users and can be widely used in various location based applications.
2021, 46(12): 1881-1888.
doi: 10.13203/j.whugis20210238
Abstract:
Objectives Real-time tropospheric mapping functions can be used into real time global navigation satellite system (GNSS) parameter estimation and applications, and it is therefore of great significance to evaluate their modeling accuracy and precise point positioning (PPP) performance. Methods In this paper, the slant total delay (STD) modeling accuracy of the latest generation of real time mapping functions, including global pressure and temperature 3 (GPT3), grid-wise forecast vienna mapping function 3 (VMF3-FC) and site-wise VMF3-FC, is evaluated by taking ray-traced STD as a reference, and the BDS3 PPP performance of the three mapping functions is analyzed on the coordinate accuracy. Results The results show that the site-wise VMF3-FC persists the best performance with STD accuracy of 2.3 cm at the 30° elevation angle and BDS3 PPP height accuracy of 9.8 mm, and it is recommended to use at the international GNSS service(IGS) stations with the site-wise products. The performance of the grid-wise VMF3-FC is a little worse with the STD accuracy of 2.6 cm and height accuracy of 10.2 mm, and it is recommended as the backup of the site-wise VMF3-FC products. The GPT3 model has the worst performance with the STD accuracy of 7.0 cm and height accuracy of 10.5 mm, while it can be embedded into the positioning software, so it is recommended to use in the case of without the need of external products.
2021, 46(12): 1889-1899.
doi: 10.13203/j.whugis20210466
Abstract:
Objectives In the localization method based on underwater acoustic arrays, noise affects the localization accuracy mainly. A localization method using a two-stage learning model is proposed to minimize noise's influence on the localization results. Methods Firstly, a learning model based on a multilayer convolutional neural network is built for noise reduction by training the real and imaginary features of the received signal separately. Secondly, an improved weighted delay summation beamformer group model is constructed, and the gradient descent criterion adjusts the weights of each channel to obtain the optimal relative time delay and the best angle estimation. Finally, more accurate localization information is received by geometric solving. Results Simulation experimental results show that the two-stage noise reduction model has awe-inspiring noise reduction performance compared to conventional underwater acoustic array processing for angular comparisons of 30°, 100°, and 130° in a SNR (signal-to-noise ratio) environment in the range of -25 dB to 10 dB. Lake tests show the same advantage of the proposed method in terms of RMSE (root mean square error) of positioning results. Conclusions The two-stage learning model has excellent robustness in coping with SNR variations, and the received signal after processing by noise reduction has higher localization accuracy after processing based on an improved time-delay and angle estimation model, which can also be applied in practical applications such as indoor acoustic localization and radio radar arrays.
2021, 46(12): 1900-1906.
doi: 10.13203/j.whugis20210234
Abstract:
Objectives With the development of the global navigation satellite system (GNSS), the number of visible satellites has increased, and the constellation configuration has been improved. While improving user observation information, multi-system GNSS positioning increases the risk of multiple gross errors, posing a threat to the integrity of the system and restricting the application of GNSS in complex environments. A common method, the receiver autonomous integrity monitoring (RAIM) to ensure the reliability of positioning based on single error, but the reliability of positioning would decline in the case of multiple gross errors. The problem of poor detection and recognition, based on the correlation analysis method of the post-test residual vector, the residual vector is affected by multiple gross errors, showing that the correlation with the observed feature vector of gross errors is weakened. The phenomenon makes gross error detection distortion. The aim of the study is to improving the accuracy of multiple gross errors detection by a new RAIM algorithm based on the density center of observed dataset. Methods This paper proposes a correlation analysis method based on the density center of the observation data set to realize the detection and identification of multiple gross errors. Firstly, we construct the observation dataset through the QR calibration method. Then, we estimate the density center by the improved Mean Shift model. Finally, we test the correlation distance between the observation feature points and the density center for detection and recognition of multiple gross errors was compared. Result The gross errors are simulated by the factual observation data, and the correlation distance of density center to gross error satellite and normal satellite, In the case of a single gross error, two gross errors, and three gross errors, the average difference correlation distance are 1.122 m and 1.516 m, 1.021 m and 1.266 m, 1.177 m and 1.588 m respectively. Compared the correlation distance of QR test vector to gross error satellite and normal satellite, the average difference correlation distance are 0.639 m and 1.142 m, 0.497 m and 0.510 m, 0.108 m and 0.198 m respectively. Conclusions The new RAIM algorithm overcomes the problem of gross error detection distortion caused by the reduced correlation between the calibration vector and the observation vector in the presence of multiple gross errors, which can effectively improve the reliability of multi-GNSS positioning.
2021, 46(12): 1907-1915.
doi: 10.13203/j.whugis20210275
Abstract:
Objectives Mobile phone positioning is a widely used approach for navigation, which has broad application prospects. The global navigation satellite system (GNSS) is widely used in smartphone positioning, but its performance can be degraded in urban canyons because of signal reflections or blockages. Shadow matching (SM) based on the three-dimensional (3D) city model can effectively improve positioning accuracy in cross-street direction. However, variation of signal-to-noise ratio (SNR) is large using smartphone for GNSS signal reception while the conventional method fails to distinguish neighboring streets, hence, greater cross-street errors. Methods This paper proposes an improved SM method together with SNR smoothing implemented in smartphones to improve the positioning accuracy in urban canyons. Firstly, a SNR smoothing method based on low-pass filter is proposed to mitigate the variation, and further to improve the correctness and stability of the visibility classification based on observations. On this basis, an improved SM, namely cluster shadow matching (Cluster-SM), is proposed, in which, the effective candidate points are clustered related to their locations. Results Experiment results showed that SNR smoothing reduces error rate of the SNR classification from 5% -30% to 0% -20%, while the implementation of optimization Cluster-SM based on SNR filtering significantly improve the GNSS positioning accuracy from 19.4 m to 2.1 m in dynamic test, compared to conventional method. Conclusions This shows the effectiveness of the novel approach in increasing positioning accuracy with the ability to distinguish neighboring streets, which provides opportunities to implement the smartphones in location-based services applications, pedestrian positioning or vehicle navigation which requires a higher positioning accuracy.
2021, 46(12): 1916-1923.
doi: 10.13203/j.whugis20210313
Abstract:
Objectives Accurate and stable extrinsic parameter calibrations are the basis of fusing perception and positioning of cameras and light detection and ranging (LiDAR). To address the problems of the Dhall calibration method in constraint accuracy, stability, and convergence, this paper proposed a LiDAR and camera calibration method with joint point-line-plane constraints. Methods First, line-line and plane-plane constraints were added, and the Kabsch method was used to obtain a closed-form solution, which reduced the influence of noise in the single corner constraint and improved the accuracy and stability of the calibration results. Then, multi-frame point clouds were superimposed to fit lines, and the fitting corners were averaged, which further reduced the influence of the point cloud noise, thus improving the convergence speed and stability of the calibration method. Results Experimental results show that the proposed method can converge within 20 frames, and the reprojection error of the corner points was less than 2 cm. Conclusions The proposed method has faster convergence speed and higher accuracy than the original method.
2021, 46(12): 1924-1931.
doi: 10.13203/j.whugis20210253
Abstract:
Objectives When the global navigation satellite system reflectometry (GNSS-R) technique is applied for sea surface wind speed retrieval, continuous geophysical model functions are often used to fit the observable extracted from the delay/Doppler map (DDM) to wind speed empirically. A large systematic deviation exists in the above method due to the few samples at 0-5 m/s and 12-20 m/s. To solve the problem, an adaptively cumulative distribution function (CDF) matching method for bias correction is proposed. Methods In this method, CDF matching is performed between the retrieved and reference wind speed sequences, and the least square method is used to adaptively find the optimal polynomial to fit the wind bias sequence and correct it. Public data products are used for validation. Results Test results show that the root mean square errors (RMSEs) after correction are reduced by 6% and 15%, and the biases are reduced by 45% and 25% at 0-5 m/s and 12-20 m/s respectively. And overall bias is improved by 25%. Conclusions Wind retrieval accuracy is obviously improved for wind ranges with few samples, and the probability distribution of wind speed retrievals is more consistent with that in nature. However, the bad performance at high wind speeds shows that the tendencies from DDM observable to wind speed at low and high wind speeds are extremely different because of the low sensitivity of DDM observables at high winds. The piecewise function is a good choice, but it is difficult to determine the piecewise point and keep it smooth.
2021, 46(12): 1932-1940.
doi: 10.13203/j.whugis20210260
Abstract:
Objectives The frequency division multiple access technology of GLONASS causes IFB (inter-frequency bias) when the receiver receives signals from different satellites, which makes it difficult to fix the ambiguity of GLONASS. It is an important factor that makes the orbit accuracy of GLONASS inferior to that of GPS(global positioning system). At present, only CODE (centre for orbit determination in Europe) among the IGS (international GNSS service) analysis centers provides GLONASS ambiguity-fixed orbits. To improve the orbit accuracy of GLONASS, it is necessary to develop a method to fix ambiguity in GLONASS all baseline and all constellation orbit determination. Methods Based on a GLONASS ambiguity fixing method for long baseline, GLONASS ultra-rapid orbit determination is propsed. Firstly, the float ionosphere-free ambiguity with a wavelength of about 5.3 cm is solved. Then the Inter-frequency phase bias (IFPB) rate will be determined by interval search method to correct the IFPB. After the IFPB is eliminated, the double difference ionosphere-free ambiguity can be fixed and then the ambiguity-fixed ultra-rapid orbit is obtained. Results and Conclusions The effect of this method in the GLONASS ultra-rapid orbit determination is verified and analyzed by using globally distributed stations. The results show that the IFPB rates are relatively stable within one month. The fixing-rate of GLONASS satellite ambiguity after correcting IFPB decreases with the increase of baseline length. The highest fixing-rate is 95%, the lowest is 88%, and the average is 94%. After the ambiguity is fixed, compared with the CODE product, the accuracy of the ultra-rapid orbit is increased by 26.8% and 18% for the calculation part and the 3‒6 hour forecast part respectively, the accuracy of the inner precision is increased by 20% and 13% for the calculation part and the 3‒6 hour forecast part respectively, which indicates that this method can effectively fix the GLONASS ambiguity and improve the accuracy of ultra-rapid orbit.
2021, 46(12): 1941-1950.
doi: 10.13203/j.whugis20210280
Abstract:
Objectives The demand for smartphones to provide stable high accuracy navigation and positioning service is increasing. However, limited by low-cost and low-power hardware of smartphones, there are high pseudo-range noise, serious multipath effects and frequent cycle slips, which lead to poor positioning performance. Methods The paper supposes a velocity-constraint dual-frequency RTD(real time differential)/RTK(real time kinematic) automatic switching navigation model. Firstly, L5/E5a/B2 signals are more resistant to multipath effects and help to improve the positioning performance. Secondly, the multipath noise is smoothed effectively by using velocity-constraint filtering model. Finally, RTD/RTK automatic switching strategy improves the stability and accuracy of positioning, especially under urban environment where continuous carrier phase measurements are insufficient. Results and Conclusions This paper assesses the positioning performance of the Huawei Mate40 and the results show that the fixed rates of static positioning and pedestrian positioning are 99.67% and 57.99% respectively. Horizontal positioning accuracy of the pedestrian test is about 0.5 m. The vehicle positioning results show that more than 70% epochs can reach lane-level accuracy, which shows that the method supposed in this paper can basically meet the smartphone users' needs of high accuracy navigation and positioning service under urban environment.
Articles online first have been peer-reviewed and accepted, which are not yet assigned to volumes /issues, but are citable by Digital Object Identifier (DOI).
Display Method:
, Available online ,
doi: 10.13203/j.whugis20210618
Abstract:
Objectives: Integrated remote sensing application based on the space-air-ground investigation system has been promoted nationwide and has achieved certain results. However, as the work progresses, some urgent problems have been exposed, such as how to define hidden hazards and what types of them, what are the targeted identification technologies, and what is the focus of the next step, etc. Methods: By summarizing a large number of practical cases, the definition of hidden landslide hazards is further clarified. Potential landslides are divided into three categories:Deformation area, historical deformation failure area and potentially unstable slope, and proposed targeted identification techniques for different types. Results: The deformation zone is mainly identified by combining multi-temporal high resolution optical images and synthetic aperture radar interferometry technology (InSAR), high-resolution optical remote sensing images and light detection and ranging (LiDAR)can be used in the historical deformation and destruction region. Conclusions: Potential unstable slopes are difficult to identify by remote sensing and traditional artificial investigations. It is necessary to vigorously develop aerial and semi-aerial geophysical prospecting technology, identify the underground structure of the slope quickly, especially the spatial distribution of the base-cover interface and the groundwater status, and delineate dangerous areas through quantitative analysis of stability.
Objectives: Integrated remote sensing application based on the space-air-ground investigation system has been promoted nationwide and has achieved certain results. However, as the work progresses, some urgent problems have been exposed, such as how to define hidden hazards and what types of them, what are the targeted identification technologies, and what is the focus of the next step, etc. Methods: By summarizing a large number of practical cases, the definition of hidden landslide hazards is further clarified. Potential landslides are divided into three categories:Deformation area, historical deformation failure area and potentially unstable slope, and proposed targeted identification techniques for different types. Results: The deformation zone is mainly identified by combining multi-temporal high resolution optical images and synthetic aperture radar interferometry technology (InSAR), high-resolution optical remote sensing images and light detection and ranging (LiDAR)can be used in the historical deformation and destruction region. Conclusions: Potential unstable slopes are difficult to identify by remote sensing and traditional artificial investigations. It is necessary to vigorously develop aerial and semi-aerial geophysical prospecting technology, identify the underground structure of the slope quickly, especially the spatial distribution of the base-cover interface and the groundwater status, and delineate dangerous areas through quantitative analysis of stability.
, Available online ,
doi: 10.13203/j.whugis20210215
Abstract:
Objectives: Global navigation satellite system (GNSS) coordinate time series provide important data support for the study of crustal movement and deformation, and plate tectonics. Due to the noise caused by various external factors, the GNSS coordinate time series cannot reflect the real motion information of the station well. To effectively reduce the noise in the GNSS time series, we adopted a noise-reduction method, GA-VMD, combining genetic algorithm (GA) and variational mode decomposition (VMD). Methods: Firstly, the genetic algorithm was used to optimize VMD parameters, and the envelope entropy of the input signal was used as the fitness function of the genetic algorithm to find the optimal VMD parameter combination suitable for the signal. According to the optimized parameters, the signal was decomposed by VMD to obtain A series of modal components. Then we calculated the multi-scale permutation entropy (MPE) of each component and then regarded the MPE as the criterion of the noise component. Finally, according to the MPE, the noise components were identified and removed, and the remaining components were reconstructed to obtain the noise-reduced signal. In this paper, the noise reduction effect of GA-VMD was analyzed through the example of noise reduction of analog signal and observation data, and compared with wavelet denoising (WD) and empirical mode decomposition (EMD) methods. Results: The results show that:(1) the noise reduction results from the analog signals show that WD and EMD have the incomplete and excessive troubles on the noise reduction, respectively. However, GA-VMD can effectively eliminate noise and retain effective signals. From the evaluation index, compared with WD and EMD, the signal-to-noise ratio were increased by 5.18dB and 2.91dB, the correlation coefficient by 0.05 and 0.02, respectively, when using GA-VMD. (2) For the complex observation, we used the noise and velocity uncertainty as accuracy indicators to evaluate the noise reduction effects of the three methods. The results show that WD can only extract a part of the white noise, while EMD and GA-VMD can completely remove the white noise. GA-VMD can reduce the flicker noise to the range of 0 to 6 mm·a-0.25. For the velocity uncertainty, the average gain rates of GA-VMD relative to the WD and EMD is 69% and 15.33%, respectively. GA-VMD has an average correction rate of 79.89% and 84.46% for the velocity uncertainty and flicker noise of GNSS coordinate time series. Conclusion: Therefore, GA-VMD is the most effective one among the three noise reduction methods, which can effectively reduce the noise in the GNSS time series and improve its accuracy. However, in this paper, we only discussed the effect of GA on VMD parameter optimization without comparing it with other method. Hence, it will be the key for studying the advantages and shortcomings of those optimization algorithms in the selection of VMD, and improving the accuracy on the GNSS time series in the future.
Objectives: Global navigation satellite system (GNSS) coordinate time series provide important data support for the study of crustal movement and deformation, and plate tectonics. Due to the noise caused by various external factors, the GNSS coordinate time series cannot reflect the real motion information of the station well. To effectively reduce the noise in the GNSS time series, we adopted a noise-reduction method, GA-VMD, combining genetic algorithm (GA) and variational mode decomposition (VMD). Methods: Firstly, the genetic algorithm was used to optimize VMD parameters, and the envelope entropy of the input signal was used as the fitness function of the genetic algorithm to find the optimal VMD parameter combination suitable for the signal. According to the optimized parameters, the signal was decomposed by VMD to obtain A series of modal components. Then we calculated the multi-scale permutation entropy (MPE) of each component and then regarded the MPE as the criterion of the noise component. Finally, according to the MPE, the noise components were identified and removed, and the remaining components were reconstructed to obtain the noise-reduced signal. In this paper, the noise reduction effect of GA-VMD was analyzed through the example of noise reduction of analog signal and observation data, and compared with wavelet denoising (WD) and empirical mode decomposition (EMD) methods. Results: The results show that:(1) the noise reduction results from the analog signals show that WD and EMD have the incomplete and excessive troubles on the noise reduction, respectively. However, GA-VMD can effectively eliminate noise and retain effective signals. From the evaluation index, compared with WD and EMD, the signal-to-noise ratio were increased by 5.18dB and 2.91dB, the correlation coefficient by 0.05 and 0.02, respectively, when using GA-VMD. (2) For the complex observation, we used the noise and velocity uncertainty as accuracy indicators to evaluate the noise reduction effects of the three methods. The results show that WD can only extract a part of the white noise, while EMD and GA-VMD can completely remove the white noise. GA-VMD can reduce the flicker noise to the range of 0 to 6 mm·a-0.25. For the velocity uncertainty, the average gain rates of GA-VMD relative to the WD and EMD is 69% and 15.33%, respectively. GA-VMD has an average correction rate of 79.89% and 84.46% for the velocity uncertainty and flicker noise of GNSS coordinate time series. Conclusion: Therefore, GA-VMD is the most effective one among the three noise reduction methods, which can effectively reduce the noise in the GNSS time series and improve its accuracy. However, in this paper, we only discussed the effect of GA on VMD parameter optimization without comparing it with other method. Hence, it will be the key for studying the advantages and shortcomings of those optimization algorithms in the selection of VMD, and improving the accuracy on the GNSS time series in the future.
, Available online ,
doi: 10.13203/j.whugis20210114
Abstract:
Objectives: With the improvement of geodetic observation accuracy, higher requirements are put forward for the seismic inversion algorithm. Methods: In view of this problem, we successfully develop a novel Grey Wolf Optimization (GWO) algorithm to invert the seismic source parameters. The weighted distance Grey Wolf Optimization (wdGWO) algorithm with the strategy of the nonlinear decreasing convergence factor based on the cosine law is proposed to instead that of the original linear decreasing. Subsequently, a combination approach with the improved wdGWO algorithm and the Simplex algorithm is configured and the introduction of the latter algorithm is to stabilize the performance of the proposed wdGWO algorithm. Thus, the combination algorithm has better advantages for both convergence and stability. Finally, we achieve synthetic tests to evaluate the performance of the basic wdGWO algorithm, the genetic algorithm and the combination algorithm. Results: The simulated experimental results show that the estimation of seismic source parameters via the proposed algorithm is superior to the wdGWO algorithm, which expresses excellent stability and accuracy. On the other hand, the stability of seismic source parameters is validated between the combination algorithm and the genetic algorithm, and we find the superiority of the combination algorithm. Furthermore, the availability of the combination algorithm is tested by the 2014 Napa earthquake and the 2017 Bodrum-Kos earthquake. The results show that the combination algorithm can achieve the inversion precision of genetic algorithm, and exhibit better parameters stability. Conclusions: Considering the accuracy and stability of the inversion results is particularly important for the accurate determination of seismic source parameters, the combination algorithm has potential applications in the inversion of seismic source parameters.
Objectives: With the improvement of geodetic observation accuracy, higher requirements are put forward for the seismic inversion algorithm. Methods: In view of this problem, we successfully develop a novel Grey Wolf Optimization (GWO) algorithm to invert the seismic source parameters. The weighted distance Grey Wolf Optimization (wdGWO) algorithm with the strategy of the nonlinear decreasing convergence factor based on the cosine law is proposed to instead that of the original linear decreasing. Subsequently, a combination approach with the improved wdGWO algorithm and the Simplex algorithm is configured and the introduction of the latter algorithm is to stabilize the performance of the proposed wdGWO algorithm. Thus, the combination algorithm has better advantages for both convergence and stability. Finally, we achieve synthetic tests to evaluate the performance of the basic wdGWO algorithm, the genetic algorithm and the combination algorithm. Results: The simulated experimental results show that the estimation of seismic source parameters via the proposed algorithm is superior to the wdGWO algorithm, which expresses excellent stability and accuracy. On the other hand, the stability of seismic source parameters is validated between the combination algorithm and the genetic algorithm, and we find the superiority of the combination algorithm. Furthermore, the availability of the combination algorithm is tested by the 2014 Napa earthquake and the 2017 Bodrum-Kos earthquake. The results show that the combination algorithm can achieve the inversion precision of genetic algorithm, and exhibit better parameters stability. Conclusions: Considering the accuracy and stability of the inversion results is particularly important for the accurate determination of seismic source parameters, the combination algorithm has potential applications in the inversion of seismic source parameters.
, Available online ,
doi: 10.13203/j.whugis20200167
Abstract:
The leveling and GNSS results are provided important data for studying the vertical movement in mainland. Give full play to the advantages of high-precision leveling points and uniform distribution of GNSS points and help improve the reliability of the vertical movement model. In the process of fusion, that the lack of coincidence points between leveling and GNSS causes joint adjustment of velocity fusion to fail, a fusion method based on final model is proposed. The method consists of two steps. Firstly, establish respective vertical movement model by leveling and GNSS with the method of inverse distance to a power. Secondly, fuse two types of models by weighted average according to the grid point precision and nearest point principle depending on the distance between grid point and measured point. Aiming at the problem that the weight associated with the distance and the velocity precision affects the final model when inverse distance to a power is applied, a method of multiplying the weight is proposed to determine reasonable weight value for each factor. The vertical movement model of Chinese mainland is established by comprehensively using the national first order leveling result, national GNSS geodetic control network and so on. In order to measure the improvement of GNSS results on the vertical movement model, utilize 20% of the modeling points selected from leveling and GNSS points uniformly to statistics the precision of leveling and fused vertical movement model. The results show that the fused vertical movement model has increased by 35.3% in Tibet, 53.6% in other region of China and 50.8% in Chinese mainland. Therefore, GNSS results can improve the precision and accuracy of the leveling vertical movement model, which is especially obvious inside the leveling loop. According to fused vertical movement model, the characteristic of vertical movement is analyzed:North China Plain and Jiangsu-shanghai area are severe subsiding, where the velocity of individual areas is up to 100 mm/a, the North-east of China and Tibet areas are uplifting, and the maximum velocity exceeds 5 mm/a in some local area, the vertical movement in other areas is relatively stable.
The leveling and GNSS results are provided important data for studying the vertical movement in mainland. Give full play to the advantages of high-precision leveling points and uniform distribution of GNSS points and help improve the reliability of the vertical movement model. In the process of fusion, that the lack of coincidence points between leveling and GNSS causes joint adjustment of velocity fusion to fail, a fusion method based on final model is proposed. The method consists of two steps. Firstly, establish respective vertical movement model by leveling and GNSS with the method of inverse distance to a power. Secondly, fuse two types of models by weighted average according to the grid point precision and nearest point principle depending on the distance between grid point and measured point. Aiming at the problem that the weight associated with the distance and the velocity precision affects the final model when inverse distance to a power is applied, a method of multiplying the weight is proposed to determine reasonable weight value for each factor. The vertical movement model of Chinese mainland is established by comprehensively using the national first order leveling result, national GNSS geodetic control network and so on. In order to measure the improvement of GNSS results on the vertical movement model, utilize 20% of the modeling points selected from leveling and GNSS points uniformly to statistics the precision of leveling and fused vertical movement model. The results show that the fused vertical movement model has increased by 35.3% in Tibet, 53.6% in other region of China and 50.8% in Chinese mainland. Therefore, GNSS results can improve the precision and accuracy of the leveling vertical movement model, which is especially obvious inside the leveling loop. According to fused vertical movement model, the characteristic of vertical movement is analyzed:North China Plain and Jiangsu-shanghai area are severe subsiding, where the velocity of individual areas is up to 100 mm/a, the North-east of China and Tibet areas are uplifting, and the maximum velocity exceeds 5 mm/a in some local area, the vertical movement in other areas is relatively stable.
, Available online ,
doi: 10.13203/j.whugis20200137
Abstract:
The registration of remote sensing image data and GIS vector data is the basis of the integration of remote sensing and GIS, which is widely used in the fields of map data update, city monitoring, map change detection and so on. At present, the key to the registration of remote sensing images and vector data is the extraction of remote sensing image features. However, the existing remote sensing image feature extraction has problems such as incomplete feature extraction, which leads to registration failure or low accuracy. This paper proposes a registration method for remote sensing images and vector data based on Mask Region-based Convolutional Neural Network (Mask R-CNN). First, select the road intersection as the distinctive feature of the same name in the remote sensing image and vector data, and create a road intersection image data set train Mask R-CNN model. Then according to the geometric topological relationship, the vector data road intersections are selected as vector control points. And take the intersection control point of the vector data as the center, use 400 pixels*400 pixels window to crop the remote sensing image data and input it into the Mask R-CNN model, extract the border of the road intersection in the image. The control points of the same name are determined according to the Euclidean distance between the remote sensing image and the vector data control points, and the control points of the same name are cleaned using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Finally, the affine transformation parameters are calculated according to the filtered control points of the same name to realize the registration of remote sensing image and vector data. The registration data of Shanghai vector data and Gaofen No.2 image data were selected for registration experiment. The average deviation of the data in the experimental area before registration was 15.34m, and the average deviation of the data after registration was reduced to 1.44m based on the method in this paper. This method can correctly register remote sensing images and vector data, and the error after registration is significantly reduced compared to before registration. Experimental results show that the proposed method has better application prospects in urban data registration, and has the characteristics of strong robustness and high accuracy. In the next step, the amount of sample data of remote sensing images will be increased, including types of remote sensing images and types of features. The method in the paper can automatically register remote sensing images and vector data from different sources in areas such as mountains, grasslands, and deserts.
The registration of remote sensing image data and GIS vector data is the basis of the integration of remote sensing and GIS, which is widely used in the fields of map data update, city monitoring, map change detection and so on. At present, the key to the registration of remote sensing images and vector data is the extraction of remote sensing image features. However, the existing remote sensing image feature extraction has problems such as incomplete feature extraction, which leads to registration failure or low accuracy. This paper proposes a registration method for remote sensing images and vector data based on Mask Region-based Convolutional Neural Network (Mask R-CNN). First, select the road intersection as the distinctive feature of the same name in the remote sensing image and vector data, and create a road intersection image data set train Mask R-CNN model. Then according to the geometric topological relationship, the vector data road intersections are selected as vector control points. And take the intersection control point of the vector data as the center, use 400 pixels*400 pixels window to crop the remote sensing image data and input it into the Mask R-CNN model, extract the border of the road intersection in the image. The control points of the same name are determined according to the Euclidean distance between the remote sensing image and the vector data control points, and the control points of the same name are cleaned using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Finally, the affine transformation parameters are calculated according to the filtered control points of the same name to realize the registration of remote sensing image and vector data. The registration data of Shanghai vector data and Gaofen No.2 image data were selected for registration experiment. The average deviation of the data in the experimental area before registration was 15.34m, and the average deviation of the data after registration was reduced to 1.44m based on the method in this paper. This method can correctly register remote sensing images and vector data, and the error after registration is significantly reduced compared to before registration. Experimental results show that the proposed method has better application prospects in urban data registration, and has the characteristics of strong robustness and high accuracy. In the next step, the amount of sample data of remote sensing images will be increased, including types of remote sensing images and types of features. The method in the paper can automatically register remote sensing images and vector data from different sources in areas such as mountains, grasslands, and deserts.
, Available online ,
doi: 10.13203/j.whugis20190475
Abstract:
Objectives:The global navigation satellite system (GNSS)) radio occultation (RO) signal can be reflected on the earth surface during the propagation process. It is of great value to explore atmospheric information contained in the reflected occultation signal to improve the accuracy of weather forecast and climate monitoring. Methods:Using the constellation observing system for meteorology, ionosphere and climate (COSMIC) occultation data collected from January 2011 to December 2015 in China, this paper extracted the occultation profile containing the reflected signals, which were strong at oceans and weak at inland and also showed seasonal changes. It was found that the snowy areas in the inland and the ocean surface were more easily to reflect the occultation signals. Further, the offshore areas of China were set into 5°×5° grids and the correlation between the percentage of occultation reflected signals and the reanalysis data from European Centre for Medium-Range Weather Forecasts (ECMWF) in each grid was analyzed. Results:Around the offshore areas to the north of 10°N, the water vapor and temperature are negatively correlated with the reflected signal percentage of occultation profile, with a correlation coefficient from -0.28 to -0.58. However, to the south of 10°N, affected by the monsoon at the south China sea, the percentage of occultation profile reflection signal is positively correlated with the reanalyzed water vapor data, with a correlation coefficient from 0.32 to 0.34, but not significantly correlated with the reanalyzed temperature data. Conclusions:The RO reflected signals associate with atmospheric conditions, and it can be applied to the direct signals as supplementary data.
Objectives:The global navigation satellite system (GNSS)) radio occultation (RO) signal can be reflected on the earth surface during the propagation process. It is of great value to explore atmospheric information contained in the reflected occultation signal to improve the accuracy of weather forecast and climate monitoring. Methods:Using the constellation observing system for meteorology, ionosphere and climate (COSMIC) occultation data collected from January 2011 to December 2015 in China, this paper extracted the occultation profile containing the reflected signals, which were strong at oceans and weak at inland and also showed seasonal changes. It was found that the snowy areas in the inland and the ocean surface were more easily to reflect the occultation signals. Further, the offshore areas of China were set into 5°×5° grids and the correlation between the percentage of occultation reflected signals and the reanalysis data from European Centre for Medium-Range Weather Forecasts (ECMWF) in each grid was analyzed. Results:Around the offshore areas to the north of 10°N, the water vapor and temperature are negatively correlated with the reflected signal percentage of occultation profile, with a correlation coefficient from -0.28 to -0.58. However, to the south of 10°N, affected by the monsoon at the south China sea, the percentage of occultation profile reflection signal is positively correlated with the reanalyzed water vapor data, with a correlation coefficient from 0.32 to 0.34, but not significantly correlated with the reanalyzed temperature data. Conclusions:The RO reflected signals associate with atmospheric conditions, and it can be applied to the direct signals as supplementary data.
, Available online ,
doi: 10.13203/j.whugis20220037
Abstract:
Objectives: Two earthquakes struck Menyuan, Qinghai province in Northwest China:Mw 5.9 event on 26 January 2016 and Mw 6.7 event on 8 January 2022, with a distance less than 40 km. Methods: The interferometric synthetic radar (InSAR) technique was utilized to process European Space Agency's Sentinel-1A radar images from both ascending and descending tracks to retrieve high resolution co-seismic surface displacement maps for both events. The co-seismic surface displacements were then inverted to determine the fault geometry parameters and the nonuniform fault slip distributions with a dislocation model in an elastic half-space. Results: Our results show that the 2016 earthquake is a reverse event on a buried fault with a maximum surface displacement of 6.7 cm and 7.0 cm on the ascending and descending tracks, respectively. The maximum slip on the fault plane reached 0.53 m and concentrated at depths of 4-12 km. The 2022 event ruptured along an NWW-SEE strike with a maximum surface displacement of 78 cm in the satellite radar line of sight and a maximum fault slip of 3.5 m occurred at a depth of about 4 km. Conclusions: Considering the tectonic settings around the Lenglong Ling fault zone, the 2022 event largely occurred on the western segment of the left-lateral Lenglong ling fault, with its western end extending westward to the Tuo Lai Shan fault. By calculating the coulomb failure stress changes (CFS), it is inferred that the 2016 event generated considerable positive CFS which might have triggered the 2022 event.
Objectives: Two earthquakes struck Menyuan, Qinghai province in Northwest China:Mw 5.9 event on 26 January 2016 and Mw 6.7 event on 8 January 2022, with a distance less than 40 km. Methods: The interferometric synthetic radar (InSAR) technique was utilized to process European Space Agency's Sentinel-1A radar images from both ascending and descending tracks to retrieve high resolution co-seismic surface displacement maps for both events. The co-seismic surface displacements were then inverted to determine the fault geometry parameters and the nonuniform fault slip distributions with a dislocation model in an elastic half-space. Results: Our results show that the 2016 earthquake is a reverse event on a buried fault with a maximum surface displacement of 6.7 cm and 7.0 cm on the ascending and descending tracks, respectively. The maximum slip on the fault plane reached 0.53 m and concentrated at depths of 4-12 km. The 2022 event ruptured along an NWW-SEE strike with a maximum surface displacement of 78 cm in the satellite radar line of sight and a maximum fault slip of 3.5 m occurred at a depth of about 4 km. Conclusions: Considering the tectonic settings around the Lenglong Ling fault zone, the 2022 event largely occurred on the western segment of the left-lateral Lenglong ling fault, with its western end extending westward to the Tuo Lai Shan fault. By calculating the coulomb failure stress changes (CFS), it is inferred that the 2016 event generated considerable positive CFS which might have triggered the 2022 event.
, Available online ,
doi: 10.13203/j.whugis20200280
Abstract:
Terrestrial laser scanning (TLS), ground-based interferometric synthetic aperture radar (GB-InSAR) and unmanned aerial vehicle photography (UAV) have been applied to emergency deformation monitoring of rockslide. The application leads to the new problem of 3D visualization of deformation data. Firstly, we introduced iterative closest point method (ICP) to complete the registration between the TLS point cloud and UAV terrain model. And then, geometric mapping method was applied to achieve data fusion between GB-InSAR deformation map and TLS point cloud. The mapping deviation is difficult to be corrected by conventional methods, because the lack of artificial control points on the rockslide. In this paper, visual interpretation and GB-SAR images simulation method were combined to extract the control points. Then the spatial coordinate transformation parameters were estimated base on the least square method. The transformation model was established to correct the mismatching. The proposed method was verified by the simulation data and the actual monitoring data of a landslide. The matching accuracy reaches the pixel level which meet the needs of emergency monitoring.
Terrestrial laser scanning (TLS), ground-based interferometric synthetic aperture radar (GB-InSAR) and unmanned aerial vehicle photography (UAV) have been applied to emergency deformation monitoring of rockslide. The application leads to the new problem of 3D visualization of deformation data. Firstly, we introduced iterative closest point method (ICP) to complete the registration between the TLS point cloud and UAV terrain model. And then, geometric mapping method was applied to achieve data fusion between GB-InSAR deformation map and TLS point cloud. The mapping deviation is difficult to be corrected by conventional methods, because the lack of artificial control points on the rockslide. In this paper, visual interpretation and GB-SAR images simulation method were combined to extract the control points. Then the spatial coordinate transformation parameters were estimated base on the least square method. The transformation model was established to correct the mismatching. The proposed method was verified by the simulation data and the actual monitoring data of a landslide. The matching accuracy reaches the pixel level which meet the needs of emergency monitoring.
, Available online ,
doi: 10.13203/j.whugis20200555
Abstract:
An undifferenced and uncombined integer ambiguity resolution method between BDS long-range reference stations is proposed. Firstly, the error observation equation is established directly by using the observations of different frequencies, and the relative zenith tropospheric wet delay error and ionospheric delay error are estimated by random walk strategy to increase the constraint between epochs. Then a linear calculation method of undifferenced integer ambiguity real-time is used to obtain the undifferenced integer ambiguity of all satellites in the current epoch of the reference network. It solves the problem that Ambiguity needs to be inherited or re-superimposed on normal equations in the reference star transformation. Because the information of each frequency observation is fully utilized, the influence of linear combination amplification noise on integer ambiguity fixing is avoided, and the ambiguity fixing success rate of uncombined method is much higher than that of ionosphere-free combination method. The results show that the average fixed speed of ambiguity of each reference station is 20 epochs. The integer ambiguity resolution of the carrier phase of the reference station can be realized quickly.
An undifferenced and uncombined integer ambiguity resolution method between BDS long-range reference stations is proposed. Firstly, the error observation equation is established directly by using the observations of different frequencies, and the relative zenith tropospheric wet delay error and ionospheric delay error are estimated by random walk strategy to increase the constraint between epochs. Then a linear calculation method of undifferenced integer ambiguity real-time is used to obtain the undifferenced integer ambiguity of all satellites in the current epoch of the reference network. It solves the problem that Ambiguity needs to be inherited or re-superimposed on normal equations in the reference star transformation. Because the information of each frequency observation is fully utilized, the influence of linear combination amplification noise on integer ambiguity fixing is avoided, and the ambiguity fixing success rate of uncombined method is much higher than that of ionosphere-free combination method. The results show that the average fixed speed of ambiguity of each reference station is 20 epochs. The integer ambiguity resolution of the carrier phase of the reference station can be realized quickly.
, Available online ,
doi: 10.13203/j.whugis20200700
Abstract:
Objectives: Due to the sharp decline of global ecosystem services (ESs), there is an urgent need for correct environmental governance policies. The two problems existing in the current ecological environment governance in China are needed to be solved:(i) the lack of overall planning based on the unit of ecological functional areas and (ii) the lack of in-depth understanding of the synergy and tradeoff between ESs. This study takes ecological functional areas as the research unit, to determine the Spatio-temporal changes of four ESs (grain production (GP), carbon sequestration (CS), outdoor recreation (OR), and biodiversity conservation (BC)), and to explore the formation mechanism for the synergy/tradeoff between ESs and its spatially non-stationary response to influencing factors. These factors are temperature, precipitation, sunshine, altitude, and urbanization degree. Methods: In this study, Various models and multisource data were integrated to estimate the distribution of the yield of ESs in Fujian. A "difference comparison method" is proposed to determine the spatial distribution of the synergy/tradeoff between ESs. The Moran's I was applied to determine the spatial autocorrelation of the synergy/tradeoff between ESs. The principal component analysis method is used to select influencing factors, and the geographically weighted logic regression (GWLR) is used to determine the spatial response of the synergy/tradeoff to influencing factors. Results: The main results are as follows:(1) the four ESs selected in the study (GP, CS, OR, BC) have spatial heterogeneity. Among them, the yield distribution of GP was significantly related to the administrative region, and the highest values of BC, OR, and CS appeared in the Ecological functional areas of biodiversity conservation and water conservation. (2) All the Moran's I were are greater than 0, which means the synergy/tradeoff between ESs has significant spatial autocorrelation and spatially clustered. (3) In space, there are synergy and tradeoff simultaneously between two ESs, instead of a single relationship. (4) When the spatial distribution of the yield of the two services is similar, such as BC and OR, the spatial distribution of the synergy/tradeoff between them and other services is also very similar. (5) The synergy/tradeoff between ESs showed significant responses to climate, topography, and urbanization factors. (6) The responses from the synergy/tradeoff between ESs to the influencing factors were spatially heterogeneous, and their positivity and intensity varied with space. Conclusion: The diagnosis results of the model show that the GWLR is superior to the global regression model in explaining the correlation between the synergy/tradeoffs of ESs and the influencing factors. By summarizing the law of the occurrence of the synergy/tradeoff, this paper refines the existing formation mechanism for the synergy/tradeoff between ESs:in a certain area, the area proportion of supportive land-use type and non-supporting land-use type, and the degree of their support effect for various services will make the two services distinguish between superior services and inferior services in this region. Due to the changes in land use, there is a dynamic gap between the superior services and the inferior services, which may lead to trade-offs when the gap is increasing.
Objectives: Due to the sharp decline of global ecosystem services (ESs), there is an urgent need for correct environmental governance policies. The two problems existing in the current ecological environment governance in China are needed to be solved:(i) the lack of overall planning based on the unit of ecological functional areas and (ii) the lack of in-depth understanding of the synergy and tradeoff between ESs. This study takes ecological functional areas as the research unit, to determine the Spatio-temporal changes of four ESs (grain production (GP), carbon sequestration (CS), outdoor recreation (OR), and biodiversity conservation (BC)), and to explore the formation mechanism for the synergy/tradeoff between ESs and its spatially non-stationary response to influencing factors. These factors are temperature, precipitation, sunshine, altitude, and urbanization degree. Methods: In this study, Various models and multisource data were integrated to estimate the distribution of the yield of ESs in Fujian. A "difference comparison method" is proposed to determine the spatial distribution of the synergy/tradeoff between ESs. The Moran's I was applied to determine the spatial autocorrelation of the synergy/tradeoff between ESs. The principal component analysis method is used to select influencing factors, and the geographically weighted logic regression (GWLR) is used to determine the spatial response of the synergy/tradeoff to influencing factors. Results: The main results are as follows:(1) the four ESs selected in the study (GP, CS, OR, BC) have spatial heterogeneity. Among them, the yield distribution of GP was significantly related to the administrative region, and the highest values of BC, OR, and CS appeared in the Ecological functional areas of biodiversity conservation and water conservation. (2) All the Moran's I were are greater than 0, which means the synergy/tradeoff between ESs has significant spatial autocorrelation and spatially clustered. (3) In space, there are synergy and tradeoff simultaneously between two ESs, instead of a single relationship. (4) When the spatial distribution of the yield of the two services is similar, such as BC and OR, the spatial distribution of the synergy/tradeoff between them and other services is also very similar. (5) The synergy/tradeoff between ESs showed significant responses to climate, topography, and urbanization factors. (6) The responses from the synergy/tradeoff between ESs to the influencing factors were spatially heterogeneous, and their positivity and intensity varied with space. Conclusion: The diagnosis results of the model show that the GWLR is superior to the global regression model in explaining the correlation between the synergy/tradeoffs of ESs and the influencing factors. By summarizing the law of the occurrence of the synergy/tradeoff, this paper refines the existing formation mechanism for the synergy/tradeoff between ESs:in a certain area, the area proportion of supportive land-use type and non-supporting land-use type, and the degree of their support effect for various services will make the two services distinguish between superior services and inferior services in this region. Due to the changes in land use, there is a dynamic gap between the superior services and the inferior services, which may lead to trade-offs when the gap is increasing.
, Available online ,
doi: 10.13203/j.whugis20200187
Abstract:
As BeiDou Satellite Navigation System-3 (BDS-3) starts to provide service for global users, it is possible to get global-coverage and all-time positioning service for space application using BDS alone. The performance of space-borne BDS positioning is thoroughly analyzed with the in-orbit data of GNSS Occultation Sounder (GNOS) aboard FengYun-3D (FY-3D) satellite. Firstly, the visibility and position dilution of precision (PDOP) of BDS satellites in different LEO orbits are calculated based on real BDS ephemeris, and the orbit and clock error of broadcast ephemeris and Signal-In-Space Range Error (SISRE) are studied. The results show that the global coverage usability from ground to 2000 Km height orbit has already been 100%. The mean visible BDS satellite number across the world is 50% larger than that of GPS. For BDS broadcast ephemeris, the 3-D orbit error is 1.5m and clock error is 2.4ns. SISRE is about 0.79m and the clock accuracy of BDS-3 has reached the same level of GPS. Second, the real visible satellite number, signal strength, precision of pseudo-range and position accuracy are verified with the measurement data of GNOS. The code biases of BDS-2 satellites are focused on. The in-orbit data results show that GNOS in FY-3D could get 100% positioning with BDS-2 signals in service areas, and 3-D position accuracy is 5.53m. All BDS-2 satellites including Geosynchronous Earth Orbit (GEO), Inclined Geosynchronous Orbit and Medium Earth Orbit satellites have code biases. When the elevation is less than 40 degree, the code bias of GEO is firstly measured directly. The total electron content above 836Km LEO orbit is measured using BDS dual-frequency measurements, which can cause relative pseudo-range delay of about 0.6m. The research in this paper is of great significance to the space-based application of BDS and lays the foundation for the design of space-based BDS receivers.
As BeiDou Satellite Navigation System-3 (BDS-3) starts to provide service for global users, it is possible to get global-coverage and all-time positioning service for space application using BDS alone. The performance of space-borne BDS positioning is thoroughly analyzed with the in-orbit data of GNSS Occultation Sounder (GNOS) aboard FengYun-3D (FY-3D) satellite. Firstly, the visibility and position dilution of precision (PDOP) of BDS satellites in different LEO orbits are calculated based on real BDS ephemeris, and the orbit and clock error of broadcast ephemeris and Signal-In-Space Range Error (SISRE) are studied. The results show that the global coverage usability from ground to 2000 Km height orbit has already been 100%. The mean visible BDS satellite number across the world is 50% larger than that of GPS. For BDS broadcast ephemeris, the 3-D orbit error is 1.5m and clock error is 2.4ns. SISRE is about 0.79m and the clock accuracy of BDS-3 has reached the same level of GPS. Second, the real visible satellite number, signal strength, precision of pseudo-range and position accuracy are verified with the measurement data of GNOS. The code biases of BDS-2 satellites are focused on. The in-orbit data results show that GNOS in FY-3D could get 100% positioning with BDS-2 signals in service areas, and 3-D position accuracy is 5.53m. All BDS-2 satellites including Geosynchronous Earth Orbit (GEO), Inclined Geosynchronous Orbit and Medium Earth Orbit satellites have code biases. When the elevation is less than 40 degree, the code bias of GEO is firstly measured directly. The total electron content above 836Km LEO orbit is measured using BDS dual-frequency measurements, which can cause relative pseudo-range delay of about 0.6m. The research in this paper is of great significance to the space-based application of BDS and lays the foundation for the design of space-based BDS receivers.
, Available online ,
doi: 10.13203/j.whugis20200692
Abstract:
With the advance in artificial intelligence, using high-resolution images to detect geological hazards has gradually become a research hotspot. Visual interpretation of landslides heavily relies on expert experience, and conventional automatic landslide detection approaches are sensitive to the presence of bare land, roads and other ground objects. To address these, a Mask R-CNN with simulated hard samples is presented in this paper for landslide detection and segmentation. Based on existing landslide samples, hard landslide samples are simulated by utilizing the shapes, colors, textures, and other characteristics of landslides to make each of the samples with a more complicated background. The original imagery and simulated hard samples are then fed into the Mask R-CNN for landslide detection and segmentation. Since the number of landslides is often limited in reality, small sample learning in the frequency domain is also presented in this paper to reduce the number of input samples while ensuring the accuracy of detection and segmentation. The experimental results in Bijie, Guizhou Province, showed that the detection and the average pixel segmentation accuracies of the proposed Mask R-CNN method with simulated hard samples are 94.0% and 90.3%, respectively. It is seen that the proposed method has high performance on landslide detection and segmentation with low false alarm rates. In addition, the performance of the proposed small-sample-based learning method in frequency domain can be improved even with a half of the data input. The effectiveness of the proposed Mask R-CNN method is further proved by the successful detection of Tianshui landslides in Gansu Province.
With the advance in artificial intelligence, using high-resolution images to detect geological hazards has gradually become a research hotspot. Visual interpretation of landslides heavily relies on expert experience, and conventional automatic landslide detection approaches are sensitive to the presence of bare land, roads and other ground objects. To address these, a Mask R-CNN with simulated hard samples is presented in this paper for landslide detection and segmentation. Based on existing landslide samples, hard landslide samples are simulated by utilizing the shapes, colors, textures, and other characteristics of landslides to make each of the samples with a more complicated background. The original imagery and simulated hard samples are then fed into the Mask R-CNN for landslide detection and segmentation. Since the number of landslides is often limited in reality, small sample learning in the frequency domain is also presented in this paper to reduce the number of input samples while ensuring the accuracy of detection and segmentation. The experimental results in Bijie, Guizhou Province, showed that the detection and the average pixel segmentation accuracies of the proposed Mask R-CNN method with simulated hard samples are 94.0% and 90.3%, respectively. It is seen that the proposed method has high performance on landslide detection and segmentation with low false alarm rates. In addition, the performance of the proposed small-sample-based learning method in frequency domain can be improved even with a half of the data input. The effectiveness of the proposed Mask R-CNN method is further proved by the successful detection of Tianshui landslides in Gansu Province.
, Available online ,
doi: 10.13203/j.whugis20200557
Abstract:
Penguins are representative organisms in Antarctica. Monitoring the population and distribution of penguins is of great significance to the study of environmental changes in Antarctica. In the past studies, due to the limitation of medium-high resolution images, the accuracy of penguin recognition is difficult to be further improved and the existing time series analysis of penguin distribution and population is based on indirect identification method. The Penguin Island in East Antarctica was selected as the study area where the Chinese Antarctic Scientific Research Team used remote sensing UAV to make aerial observations in January 2017, January 2018 and December 2019, and obtained centimeter-level resolution images. Based on object-oriented classification, the shadow pixels of penguins in 3 images were extracted, the penguin habitats were marked, and the penguin population was calculated. The overall accuracy is 91%. The experimental results showed the dynamic changes of penguin population of which the distribution of penguin habitat was relatively fixed, but the number of penguins fluctuated with 1,068 pairs, 1,003 pairs and 1,081 pairs in 3 images respectively.
Penguins are representative organisms in Antarctica. Monitoring the population and distribution of penguins is of great significance to the study of environmental changes in Antarctica. In the past studies, due to the limitation of medium-high resolution images, the accuracy of penguin recognition is difficult to be further improved and the existing time series analysis of penguin distribution and population is based on indirect identification method. The Penguin Island in East Antarctica was selected as the study area where the Chinese Antarctic Scientific Research Team used remote sensing UAV to make aerial observations in January 2017, January 2018 and December 2019, and obtained centimeter-level resolution images. Based on object-oriented classification, the shadow pixels of penguins in 3 images were extracted, the penguin habitats were marked, and the penguin population was calculated. The overall accuracy is 91%. The experimental results showed the dynamic changes of penguin population of which the distribution of penguin habitat was relatively fixed, but the number of penguins fluctuated with 1,068 pairs, 1,003 pairs and 1,081 pairs in 3 images respectively.
, Available online ,
doi: 10.13203/j.whugis20210035
Abstract:
Tianwen-1 is China's first independent interplanetary mission. It will complete orbiting, landing, and roving tasks in one operation. Exploiting tracking data from gathered during this extended mission, this paper explores possible ways to improve the Mars gravity field model through simulations. We designed two types of orbits, a polar and a near equatorial large eccentricity orbit and recovered six gravity solutions considering various error sources. By the power spectrum of these gravity models was analyzed and evaluated, finding that a month of tracking data from the polar orbit or the combined polar and near equatorial orbit could be used to properly reconstruct the Mars gravity field model with orders and degrees of 42 under the 0.1 mm/s measurement noise. The results show that after considering the influence of comprehensive error, the accuracy of gravity field solutions from the two types of orbits was similar. Nevertheless, the orbit with large eccentricity near the equator has a slightly stronger constraint on more than 35 order and degree coefficients.
Tianwen-1 is China's first independent interplanetary mission. It will complete orbiting, landing, and roving tasks in one operation. Exploiting tracking data from gathered during this extended mission, this paper explores possible ways to improve the Mars gravity field model through simulations. We designed two types of orbits, a polar and a near equatorial large eccentricity orbit and recovered six gravity solutions considering various error sources. By the power spectrum of these gravity models was analyzed and evaluated, finding that a month of tracking data from the polar orbit or the combined polar and near equatorial orbit could be used to properly reconstruct the Mars gravity field model with orders and degrees of 42 under the 0.1 mm/s measurement noise. The results show that after considering the influence of comprehensive error, the accuracy of gravity field solutions from the two types of orbits was similar. Nevertheless, the orbit with large eccentricity near the equator has a slightly stronger constraint on more than 35 order and degree coefficients.
, Available online ,
doi: 10.13203/j.whugis20200221
Abstract:
The development of ICT (Information and Communications Technology) provides new challeng⁃ es and opportunities for modern cartography. Totally different from the traditional cartography in the fields of the research methods and themes, modern cartography trends to digital cyberspace from paper map, and cartographic theories are urged to be improved. However, the understanding of the theories about carto⁃ graphic virtual space have great differences within the professional domain, and the internal contradictions and transboundary invasion make map gradually become an appurtenance of Internet products. To learning from certain domains to analyse and handle them, challenges have prompted cartographic scholars to ex⁃ plore new fields. The development of "digits and culture" has pushed the game to the forefront of hightech. Games are often geographically significant, and maps of game are a combination of ICT and maps. Based on the cyberspace in game map with instances as the research subject, the paper explores the charac⁃ teristics and essence of game map and its implications to the real physical world by the dimensionalities, in⁃ teractivity, narrativity, exploration and cultural dissemination of game maps to discover the extension of cartography, which would replenish and perfect modern cartographic theory and promote its development.
The development of ICT (Information and Communications Technology) provides new challeng⁃ es and opportunities for modern cartography. Totally different from the traditional cartography in the fields of the research methods and themes, modern cartography trends to digital cyberspace from paper map, and cartographic theories are urged to be improved. However, the understanding of the theories about carto⁃ graphic virtual space have great differences within the professional domain, and the internal contradictions and transboundary invasion make map gradually become an appurtenance of Internet products. To learning from certain domains to analyse and handle them, challenges have prompted cartographic scholars to ex⁃ plore new fields. The development of "digits and culture" has pushed the game to the forefront of hightech. Games are often geographically significant, and maps of game are a combination of ICT and maps. Based on the cyberspace in game map with instances as the research subject, the paper explores the charac⁃ teristics and essence of game map and its implications to the real physical world by the dimensionalities, in⁃ teractivity, narrativity, exploration and cultural dissemination of game maps to discover the extension of cartography, which would replenish and perfect modern cartographic theory and promote its development.
, Available online ,
doi: 10.13203/j.whugis20200172
Abstract:
Aiming at the mixed observation data of multiple distribution forms, a P-model distributed mixed model is established. Considering that the mixed number in the model belongs to incomplete data, the EM algorithm is introduced to estimate the parameters of the mixed model and the P-model mixed model parameters are derived in detail The estimated iteration formula and the corresponding iteration steps are given. Finally, the mixed Gaussian distribution data, Laplacian distribution and Gaussian distribution mixed data, and the residual data of measured GPS observations are used to verify the correctness and adaptability of the formula in this paper. The results of the calculation examples show that, compared with the single probability distribution, the P-parameter hybrid model can accurately reflect the actual situation of the data distribution, and the model parameters estimated by the EM algorithm have higher accuracy.
Aiming at the mixed observation data of multiple distribution forms, a P-model distributed mixed model is established. Considering that the mixed number in the model belongs to incomplete data, the EM algorithm is introduced to estimate the parameters of the mixed model and the P-model mixed model parameters are derived in detail The estimated iteration formula and the corresponding iteration steps are given. Finally, the mixed Gaussian distribution data, Laplacian distribution and Gaussian distribution mixed data, and the residual data of measured GPS observations are used to verify the correctness and adaptability of the formula in this paper. The results of the calculation examples show that, compared with the single probability distribution, the P-parameter hybrid model can accurately reflect the actual situation of the data distribution, and the model parameters estimated by the EM algorithm have higher accuracy.
, Available online ,
doi: 10.13203/j.whugis20200028
Abstract:
The tensor invariant method for determining the Earth's gravity field with satellite gravitational gradient data is studied. The tensor invariant method does not depend on the acquisition of the spatial orientation information of the gradiometer, so it has a special advantage over the traditional methods in determining the earth gravity field. Numerical analyses are made for the linearization of tensor invariant observation equation, the processing of non-full tensor observation data, and the accuracy of gravity field recovery with the gradient data containing white noise. The results show that the tensor invariant solution realizes the combine processing of the different tensor components. The tailored linearization effectively achieves the linearization of the tensor invariant observation model while the accuracy of gravity field model determined using tensor invariants is significantly higher than that of the single tensor component solution. Also, the choice of initial reference model exerts minor influence on the total iterative process. Due to the non-full tensor gradient observations, it is proposed to replace the low precision gradient component by using the spherical harmonic synthesis of a priori gravity field model, which will not change the convergence and the accuracy of the solution even if the white noise exists in the observations because of the nadir-pointing characteristics of the gradiometer..
The tensor invariant method for determining the Earth's gravity field with satellite gravitational gradient data is studied. The tensor invariant method does not depend on the acquisition of the spatial orientation information of the gradiometer, so it has a special advantage over the traditional methods in determining the earth gravity field. Numerical analyses are made for the linearization of tensor invariant observation equation, the processing of non-full tensor observation data, and the accuracy of gravity field recovery with the gradient data containing white noise. The results show that the tensor invariant solution realizes the combine processing of the different tensor components. The tailored linearization effectively achieves the linearization of the tensor invariant observation model while the accuracy of gravity field model determined using tensor invariants is significantly higher than that of the single tensor component solution. Also, the choice of initial reference model exerts minor influence on the total iterative process. Due to the non-full tensor gradient observations, it is proposed to replace the low precision gradient component by using the spherical harmonic synthesis of a priori gravity field model, which will not change the convergence and the accuracy of the solution even if the white noise exists in the observations because of the nadir-pointing characteristics of the gradiometer..
, Available online ,
doi: 10.13203/j.whugis20200286
Abstract:
Multi-scale boundary information can be obtained using potential field data at different scales, which having an important practical value on account of the tectonic division, geological mapping orebody delineation and the research of crystalline basement. We use the bi-dimensional empirical mode decomposition (BEMD) and the wavelet modulus maximum (WMM) method to obtain the multi-scale boundary detection of potential field data. Firstly, it is shown that the BEMD method can realize the multi-scale decomposition of potential field data according to the previous research results by the author, and the anomalies at different scales possess practical geological significance; Secondly, the model test established by us shows that WMM method has the ability of resisting the noise interference so as to detect the boundary of the combined model about the distribution, location and direction clearly and accurately using the gravity anomalies; Lastly, we also achieve the results of multi-scale decomposition of the Bouguer gravity anomalies in the Three Gorges region by BEMD method, and then estimate the approximate depth of the buried source by the radial logarithmic power spectrum of each BIMF and Res, and we deal with each bi-dimensional intrinsic mode function (BIMF) using WMM method so as to obtain the distribution characteristic of the structure and rock mass in different depths, the results of boundary detection at all levels are analyzed and explained according to the regional geology structure feature, the advantage and effectiveness of this method is verified in the end.
Multi-scale boundary information can be obtained using potential field data at different scales, which having an important practical value on account of the tectonic division, geological mapping orebody delineation and the research of crystalline basement. We use the bi-dimensional empirical mode decomposition (BEMD) and the wavelet modulus maximum (WMM) method to obtain the multi-scale boundary detection of potential field data. Firstly, it is shown that the BEMD method can realize the multi-scale decomposition of potential field data according to the previous research results by the author, and the anomalies at different scales possess practical geological significance; Secondly, the model test established by us shows that WMM method has the ability of resisting the noise interference so as to detect the boundary of the combined model about the distribution, location and direction clearly and accurately using the gravity anomalies; Lastly, we also achieve the results of multi-scale decomposition of the Bouguer gravity anomalies in the Three Gorges region by BEMD method, and then estimate the approximate depth of the buried source by the radial logarithmic power spectrum of each BIMF and Res, and we deal with each bi-dimensional intrinsic mode function (BIMF) using WMM method so as to obtain the distribution characteristic of the structure and rock mass in different depths, the results of boundary detection at all levels are analyzed and explained according to the regional geology structure feature, the advantage and effectiveness of this method is verified in the end.
, Available online ,
doi: 10.13203/j.whugis20200004
Abstract:
In order to analyze the differences between the geodetic, geocentric, rectifying, authalic, conformal latitudes and reduced latitude, symbolic expressions of difference between the auxiliary and reduced latitudes were derived by the computer algebra system and expressed by several power series in terms of the first eccentricity e and the third flattening rate n. Theoretical and numerical analyses show that the symbolic expressions in terms of the third flattening n are more concise than those of the first eccentricity e and the extrema of the differences between the auxiliary latitudes and the reduced latitude are all near π/4. Particularly, the absolute minimum and maximum values of the differences to the reduced latitude are generated from the authalic and geodetic latitudes, respectively, the new formulas presented in this study are applicable to any Earth ellipsoid and, to some extent, they enriched the analytical theory of the various commonly used latitudes.
In order to analyze the differences between the geodetic, geocentric, rectifying, authalic, conformal latitudes and reduced latitude, symbolic expressions of difference between the auxiliary and reduced latitudes were derived by the computer algebra system and expressed by several power series in terms of the first eccentricity e and the third flattening rate n. Theoretical and numerical analyses show that the symbolic expressions in terms of the third flattening n are more concise than those of the first eccentricity e and the extrema of the differences between the auxiliary latitudes and the reduced latitude are all near π/4. Particularly, the absolute minimum and maximum values of the differences to the reduced latitude are generated from the authalic and geodetic latitudes, respectively, the new formulas presented in this study are applicable to any Earth ellipsoid and, to some extent, they enriched the analytical theory of the various commonly used latitudes.
, Available online ,
doi: 10.13203/j.whugis20200007
Abstract:
The priori studies have revealed that the Chandler and annual wobbles (CW and AW) in polar motion (PM) are unstable over time. In order to further enhance the prediction accuracy of polar motion (PM), a harmonic model for the linear trend, CW and AW with time-varying coefficients is developed in this paper. The developed model takes into account the variations in both amplitudes and phases of the CW and AW. The PM predictions are calculated by means of two schemes to valid the effectives of the developed model:the least-squares extrapolation of the variable harmonic model for the linear trend, time-varying CW and AW in combination with the autoregressive technique, denoted as VLS+AR, and the combination of the least-squares extrapolation of the invariable harmonic model and AR filtering, referred to LS+AR. The results show that the accuracies of the PM predictions obtained by the VLS+AR are better than those generated by the LS+AR, especially for medium- and long-term predictions.
The priori studies have revealed that the Chandler and annual wobbles (CW and AW) in polar motion (PM) are unstable over time. In order to further enhance the prediction accuracy of polar motion (PM), a harmonic model for the linear trend, CW and AW with time-varying coefficients is developed in this paper. The developed model takes into account the variations in both amplitudes and phases of the CW and AW. The PM predictions are calculated by means of two schemes to valid the effectives of the developed model:the least-squares extrapolation of the variable harmonic model for the linear trend, time-varying CW and AW in combination with the autoregressive technique, denoted as VLS+AR, and the combination of the least-squares extrapolation of the invariable harmonic model and AR filtering, referred to LS+AR. The results show that the accuracies of the PM predictions obtained by the VLS+AR are better than those generated by the LS+AR, especially for medium- and long-term predictions.
, Available online ,
doi: 10.13203/j.whugis20200016
Abstract:
The slope filtering algorithm of point cloud is simple and easy to implement. In order to further improve the adaptability of slope filtering algorithm, a multi-scale adaptive slope filtering algorithm of point cloud is proposed. Firstly, a pseudo-grid is introduced to divide the point cloud on the basis of data pre-processing. Then, the slope angle of grid points is calculated one by one using distance weighting, and the threshold value is determined adaptively by combining k-means clustering and normal distribution. Finally, the multi-scale strategy is used to reduce the grid size step by step to achieve fine filtering of point cloud data. The algorithm was verified using two point cloud data sets with different densities. Compared with the two slope filtering algorithms and the classical algorithms of ISPRS, the experiment shows that this algorithm has better overall fitting results and higher stability, which is suitable for point cloud in different scenes.
The slope filtering algorithm of point cloud is simple and easy to implement. In order to further improve the adaptability of slope filtering algorithm, a multi-scale adaptive slope filtering algorithm of point cloud is proposed. Firstly, a pseudo-grid is introduced to divide the point cloud on the basis of data pre-processing. Then, the slope angle of grid points is calculated one by one using distance weighting, and the threshold value is determined adaptively by combining k-means clustering and normal distribution. Finally, the multi-scale strategy is used to reduce the grid size step by step to achieve fine filtering of point cloud data. The algorithm was verified using two point cloud data sets with different densities. Compared with the two slope filtering algorithms and the classical algorithms of ISPRS, the experiment shows that this algorithm has better overall fitting results and higher stability, which is suitable for point cloud in different scenes.
, Available online ,
doi: 10.13203/j.whugis20210100
Abstract:
Objectives: In the process of digital elevation model (DEM) modeling, the existing interpolation methods do not take into account the local topographic characteristics near the breaklines, which makes the elevation of the local area of the breakline smoothed, thus leading to topographic distortion. Methods: A weight function with respect to considering the characteristics of breaklines is constructed, and a weight radial basis function (RBF) method is proposed. The new method makes full use of the gradient and direction information of the sampling points near the fracture line. First, the distance between the sampling point and the point to be sought is calculated adaptively by capturing the structure tensor of each sampling point, and then the distance is used to assign a suitable weight to each sampling point, and finally the DEM modeling is realized by using weighted interpolation. Results: Real-world examples on 10 public and 1 private dataset of DEM construction with airborne LiDAR point clouds indicates that (1) the calculation results of each interpolation method gradually decrease with the decrease of sampling points. (2) Compared to RBF and the classical interpolation methods including inverse distance weighting(IDW), ordinary Kriging(OK) and constrained triangulated irregular network (TIN), our method has a better ability to maintain terriain features in the breakline area. Regardless of sample density, our method is always more accurate than the other methods. Conclusions: Overall, the proposed method with the merit of terrain feature preservation is helpful for the construction of high-accurate DEMs, which play an important role in some geoscience applications with data quality as the most important factor.
Objectives: In the process of digital elevation model (DEM) modeling, the existing interpolation methods do not take into account the local topographic characteristics near the breaklines, which makes the elevation of the local area of the breakline smoothed, thus leading to topographic distortion. Methods: A weight function with respect to considering the characteristics of breaklines is constructed, and a weight radial basis function (RBF) method is proposed. The new method makes full use of the gradient and direction information of the sampling points near the fracture line. First, the distance between the sampling point and the point to be sought is calculated adaptively by capturing the structure tensor of each sampling point, and then the distance is used to assign a suitable weight to each sampling point, and finally the DEM modeling is realized by using weighted interpolation. Results: Real-world examples on 10 public and 1 private dataset of DEM construction with airborne LiDAR point clouds indicates that (1) the calculation results of each interpolation method gradually decrease with the decrease of sampling points. (2) Compared to RBF and the classical interpolation methods including inverse distance weighting(IDW), ordinary Kriging(OK) and constrained triangulated irregular network (TIN), our method has a better ability to maintain terriain features in the breakline area. Regardless of sample density, our method is always more accurate than the other methods. Conclusions: Overall, the proposed method with the merit of terrain feature preservation is helpful for the construction of high-accurate DEMs, which play an important role in some geoscience applications with data quality as the most important factor.
, Available online ,
doi: 10.13203/j.whugis20190434
Abstract:
As a very important interpretation result of gravity anomaly, fault structure is closely related to the division of tectonic units. The full tensor gravity gradient are now widely used in geophysical domain for its large amount of information, containing higher frequency signals and better depiction the fine structure characteristics. We explore the solutions for boundary identification and extraction of fault structure in South China Sea based on full tensor gravity gradient combinations in this paper. Firstly, we apply various gravity gradients structure tensor eigenvalue, both the traditional method with three-component of gravity gradient tensor and the new method with full tensor gravity gradient combinations, to infer the distribution of the main faults in the South China sea and its adjacent area using satellite data. By comparison and analysis, traditional methods cannot be effectively balanced depth anomaly amplitude and will produce the false boundary when there are positive and negative anomalies at the same time. However, the full tensor gravity gradient combination method can not only effectively avoid the shortcomings of the traditional method but also obtain boundary with good continuity and convergence. Secondly, the edge extraction method based on Canny operator is employed to identify the position of the fractures which is accordance with the results obtained from the the full tensor gravity gradient combination method. The strike of the faults over South China Sea is mainly NE and NW, followed by NEE, NW, EW and nearly NS.
As a very important interpretation result of gravity anomaly, fault structure is closely related to the division of tectonic units. The full tensor gravity gradient are now widely used in geophysical domain for its large amount of information, containing higher frequency signals and better depiction the fine structure characteristics. We explore the solutions for boundary identification and extraction of fault structure in South China Sea based on full tensor gravity gradient combinations in this paper. Firstly, we apply various gravity gradients structure tensor eigenvalue, both the traditional method with three-component of gravity gradient tensor and the new method with full tensor gravity gradient combinations, to infer the distribution of the main faults in the South China sea and its adjacent area using satellite data. By comparison and analysis, traditional methods cannot be effectively balanced depth anomaly amplitude and will produce the false boundary when there are positive and negative anomalies at the same time. However, the full tensor gravity gradient combination method can not only effectively avoid the shortcomings of the traditional method but also obtain boundary with good continuity and convergence. Secondly, the edge extraction method based on Canny operator is employed to identify the position of the fractures which is accordance with the results obtained from the the full tensor gravity gradient combination method. The strike of the faults over South China Sea is mainly NE and NW, followed by NEE, NW, EW and nearly NS.
, Available online ,
doi: 10.13203/j.whugis20210204
Abstract:
Very Long Baseline Interferometry (VLBI) is one of the main space geodetic techniques for monitoring Earth Orientation Parameters (EOP). China is building three VLBI Global Observing System (VGOS) antennas. In order to improve the EOP measurement accuracies, it is necessary to optimize VGOS observing network by extending the domestic VGOS network to an international network. We set the three Chinese VGOS stations located at Shanghai, Urumqi and Beijing as core stations. By adding two international stations selected from four candidate sites located at Hobart of Australia or Bandung of Indonesia, as well as Johannesburg of South Africa or Hawaii of USA, we could form four different 5-station VGOS networks. The performance of EOP measurement accuracies for each network were analyzed based on generation of bulk observing schedules and subsequent large-scale Monte-Carlo simulations. We use the repeatability value defined as standard deviation of EOP estimates as an indicator to evaluate the performance of each schedule and each network. We also compared the EOP formal errors of current VGOS observing sessions to our simulation results. The results show:(1) The EOP measurement capability of the expanded 5-station networks are all much better than that of the 3-station domestic network. (2) The optimized 5-station network, which consists of 3 domestic antennas, Johannesburg in South Africa and Hobart in Australia, has the best EOP measurement results. Compared to the 3-station domestic network, the repeatability of dUT1, the pole motion XP and YP components are decreased by a factor of 5.7, 2.8 and 18.3 respectively. (3) The optimized 5-station network could reach equal or even better EOP estimates than that of the current IVS VGOS observing networks. We demonstrated that the EOP measurement accuracies can be improved by optimizing the observing network based on Monte Carlo simulations. The simulation results can be served as a start point for the future development of high-precision EOP observing program in China.
Very Long Baseline Interferometry (VLBI) is one of the main space geodetic techniques for monitoring Earth Orientation Parameters (EOP). China is building three VLBI Global Observing System (VGOS) antennas. In order to improve the EOP measurement accuracies, it is necessary to optimize VGOS observing network by extending the domestic VGOS network to an international network. We set the three Chinese VGOS stations located at Shanghai, Urumqi and Beijing as core stations. By adding two international stations selected from four candidate sites located at Hobart of Australia or Bandung of Indonesia, as well as Johannesburg of South Africa or Hawaii of USA, we could form four different 5-station VGOS networks. The performance of EOP measurement accuracies for each network were analyzed based on generation of bulk observing schedules and subsequent large-scale Monte-Carlo simulations. We use the repeatability value defined as standard deviation of EOP estimates as an indicator to evaluate the performance of each schedule and each network. We also compared the EOP formal errors of current VGOS observing sessions to our simulation results. The results show:(1) The EOP measurement capability of the expanded 5-station networks are all much better than that of the 3-station domestic network. (2) The optimized 5-station network, which consists of 3 domestic antennas, Johannesburg in South Africa and Hobart in Australia, has the best EOP measurement results. Compared to the 3-station domestic network, the repeatability of dUT1, the pole motion XP and YP components are decreased by a factor of 5.7, 2.8 and 18.3 respectively. (3) The optimized 5-station network could reach equal or even better EOP estimates than that of the current IVS VGOS observing networks. We demonstrated that the EOP measurement accuracies can be improved by optimizing the observing network based on Monte Carlo simulations. The simulation results can be served as a start point for the future development of high-precision EOP observing program in China.
, Available online ,
doi: 10.13203/j.whugis20210091
Abstract:
Satellite Based Augmentation System (SBAS) improves the positioning accuracy and integrity by broadcasting ephemeris corrections and associated integrity parameters through Geostationary Earth Orbit (GEO) satellites. SBAS GEO satellite can also be used as a ranging source together with Global Positioning System (GPS) satellites to improve the system performance. User range error (URE) of the GEO satellite and their effect on positioning results are investigated. URE of SBAS GEO and GPS satellite is determined by weighting the observation residuals which are derived with fixed station coordinates. SBAS messages are applied to correct the orbit and clock errors contained in broadcast ephemeris and the ionosphere delay. Ranging data from GEO satellite is engaged in the SBAS positioning process to explore the impact on positioning accuracy, integrity and availability. SBAS messages broadcast by Wide Area Augmentation System (WAAS), BeiDou Satellite Based Augmentation Systems (BDSBAS), GPS Aided Geo Augmented Navigation (GAGAN) and MTSAT (Multi-functional Transport Satellite) Satellite based Augmentation System (MSAS) and real data from International GNSS Service (IGS) stations are applied to perform the assessment. European Geostationary Navigation Overlay Service (EGNOS) and System for Differential Corrections and Monitoring (SDCM) are not included because of the absence of the ranging capability. It was found that WAAS GEO satellite has the best performance with ranging accuracy better than 1.6m. The 99.9% error bound is less than 6.8m while the broadcast User Differential Range Error (UDRE) for the GEO satellite is 7.5m, which meets the integrity requirement. The 3 GEO satellites of BDSBAS show ranging biases of 14.32m, 12.64m and 17.44m respectively, and the accuracy is better than 2.9m. After removing the bias, the related 99.9% error bound is 8.60m, 7.80m and 11.60m which suggests an UDRE of 11~12. An URA of 15 is broadcast in message type 9 for the BDSBAS GEO satellites. URE of the GAGAN GEO satellite is better than 13.9m and for MSAS it is better than 3.2m. The UDRE of GAGAN and MSAS is 14. URE of GPS satellite after augmented by SBAS is also calculated for comparison purpose. Ranging accuracy of GPS is 0.60m, 0.53m, 0.21m and 0.34m for WAAS, BDSBAS, GAGAN and MSAS respectively. WAAS GEO satellite is selected to perform the positioning analysis whose UDRE is less than 14 so that it can be weighted properly in the solution. Engagement of GEO satellite in SBAS positioning will lead to lower Position Dilution of Precision (PDOP) and reduce the protection level especially for the blockage circumstance. The system availability of Localizer Performance with Vertical guidance 200 (LPV200) approach is improved from 99.984% to 99.997% with collaboration of 3 GEO satellites' observation. With sufficient GPS satellites, the combination of GEO satellites will decrease the positioning accuracy because of the relative larger range error. Results suggest that SBAS GEO ranging data should be included in the SBAS solution for aviation users.
Satellite Based Augmentation System (SBAS) improves the positioning accuracy and integrity by broadcasting ephemeris corrections and associated integrity parameters through Geostationary Earth Orbit (GEO) satellites. SBAS GEO satellite can also be used as a ranging source together with Global Positioning System (GPS) satellites to improve the system performance. User range error (URE) of the GEO satellite and their effect on positioning results are investigated. URE of SBAS GEO and GPS satellite is determined by weighting the observation residuals which are derived with fixed station coordinates. SBAS messages are applied to correct the orbit and clock errors contained in broadcast ephemeris and the ionosphere delay. Ranging data from GEO satellite is engaged in the SBAS positioning process to explore the impact on positioning accuracy, integrity and availability. SBAS messages broadcast by Wide Area Augmentation System (WAAS), BeiDou Satellite Based Augmentation Systems (BDSBAS), GPS Aided Geo Augmented Navigation (GAGAN) and MTSAT (Multi-functional Transport Satellite) Satellite based Augmentation System (MSAS) and real data from International GNSS Service (IGS) stations are applied to perform the assessment. European Geostationary Navigation Overlay Service (EGNOS) and System for Differential Corrections and Monitoring (SDCM) are not included because of the absence of the ranging capability. It was found that WAAS GEO satellite has the best performance with ranging accuracy better than 1.6m. The 99.9% error bound is less than 6.8m while the broadcast User Differential Range Error (UDRE) for the GEO satellite is 7.5m, which meets the integrity requirement. The 3 GEO satellites of BDSBAS show ranging biases of 14.32m, 12.64m and 17.44m respectively, and the accuracy is better than 2.9m. After removing the bias, the related 99.9% error bound is 8.60m, 7.80m and 11.60m which suggests an UDRE of 11~12. An URA of 15 is broadcast in message type 9 for the BDSBAS GEO satellites. URE of the GAGAN GEO satellite is better than 13.9m and for MSAS it is better than 3.2m. The UDRE of GAGAN and MSAS is 14. URE of GPS satellite after augmented by SBAS is also calculated for comparison purpose. Ranging accuracy of GPS is 0.60m, 0.53m, 0.21m and 0.34m for WAAS, BDSBAS, GAGAN and MSAS respectively. WAAS GEO satellite is selected to perform the positioning analysis whose UDRE is less than 14 so that it can be weighted properly in the solution. Engagement of GEO satellite in SBAS positioning will lead to lower Position Dilution of Precision (PDOP) and reduce the protection level especially for the blockage circumstance. The system availability of Localizer Performance with Vertical guidance 200 (LPV200) approach is improved from 99.984% to 99.997% with collaboration of 3 GEO satellites' observation. With sufficient GPS satellites, the combination of GEO satellites will decrease the positioning accuracy because of the relative larger range error. Results suggest that SBAS GEO ranging data should be included in the SBAS solution for aviation users.
, Available online ,
doi: 10.13203/j.whugis20200505
Abstract:
Objectives The polar motion (PM) is an important part of the Earth rotation parameters (ERP). the prediction error of ERP can be effectively reduced by improving the prediction accuracy of PM. Methods Aiming at the complex time variation characteristics of PM, a high-precision prediction method based on the Volterra adaptive algorithm was proposed for the first time, which taken the PM series as chaos. Firstly, the maximum Lyapunov exponent was calculated by using the small data sets method. This analysis proves that the PM has chaotic characteristics. Then two experiments were performed for the second order Volterra adaptive algorithm. Results The results of the experimental results were compared with the Earth Orientation Parameters Prediction Comparison Campaign (EOP PCC) and Bulletin A, the official forecast product of IERS. The results show that the prediction accuracy of this method is higher than that of EOP PCC, and Xp component prediction accuracy is improved significantly, Yp component can be also slightly improved. Compared with Bulletin A, the accuracy of the two forecast results has advantages and disadvantages. Conclusions The example further proves that our method can obtain good forecast results in the short-term polar motion forecast, especially the prediction period is more accurate than that of the small period.
Objectives The polar motion (PM) is an important part of the Earth rotation parameters (ERP). the prediction error of ERP can be effectively reduced by improving the prediction accuracy of PM. Methods Aiming at the complex time variation characteristics of PM, a high-precision prediction method based on the Volterra adaptive algorithm was proposed for the first time, which taken the PM series as chaos. Firstly, the maximum Lyapunov exponent was calculated by using the small data sets method. This analysis proves that the PM has chaotic characteristics. Then two experiments were performed for the second order Volterra adaptive algorithm. Results The results of the experimental results were compared with the Earth Orientation Parameters Prediction Comparison Campaign (EOP PCC) and Bulletin A, the official forecast product of IERS. The results show that the prediction accuracy of this method is higher than that of EOP PCC, and Xp component prediction accuracy is improved significantly, Yp component can be also slightly improved. Compared with Bulletin A, the accuracy of the two forecast results has advantages and disadvantages. Conclusions The example further proves that our method can obtain good forecast results in the short-term polar motion forecast, especially the prediction period is more accurate than that of the small period.
, Available online ,
doi: 10.13203/j.whugis20210029
Abstract:
Global Navigation Satellite System(GNSS) position time series usually includes both tectonic and non-tectonic deformation information, have the characterizes of complex components, difficult modeling, and difficult to effectively separate the non-tectonic signals from original time series. How to remove the non-tectonic deformation information is very important for the accurate and effective application of the observation data. Empirical Mode Decomposition(EMD) is an adaptive time-frequency processing method, we use this method to correct the period term of 24 GNSS continuous station time series in Sichuan and Yunnan areas. The results show that the correction of the periodic term is necessary, EMD method is able to extract the periodic components of different frequencies and amplitudes adaptively according to each station's own characteristics, which is also more in line with the actual situation. The average Root Mean Square error(RMS) is reduced by 19.96%, 11.57% and 38.50% compared to the original time series of N, E and U direction, respectively. It is a more accurate and effective method than harmonic model correction. Then, we use the modified continuous station time series to simulate the mobile observations, found that relatively reliable motion velocities can be obtained after 5~6 years/period of observations. The stability and reliability of the EMD method were verified by correcting the period term of the mobile station with the actual continuous observation station at a closer distance, which also provides a reference and theoretical basis for the implementation of mobile GNSS observations and the correction of observation data.
Global Navigation Satellite System(GNSS) position time series usually includes both tectonic and non-tectonic deformation information, have the characterizes of complex components, difficult modeling, and difficult to effectively separate the non-tectonic signals from original time series. How to remove the non-tectonic deformation information is very important for the accurate and effective application of the observation data. Empirical Mode Decomposition(EMD) is an adaptive time-frequency processing method, we use this method to correct the period term of 24 GNSS continuous station time series in Sichuan and Yunnan areas. The results show that the correction of the periodic term is necessary, EMD method is able to extract the periodic components of different frequencies and amplitudes adaptively according to each station's own characteristics, which is also more in line with the actual situation. The average Root Mean Square error(RMS) is reduced by 19.96%, 11.57% and 38.50% compared to the original time series of N, E and U direction, respectively. It is a more accurate and effective method than harmonic model correction. Then, we use the modified continuous station time series to simulate the mobile observations, found that relatively reliable motion velocities can be obtained after 5~6 years/period of observations. The stability and reliability of the EMD method were verified by correcting the period term of the mobile station with the actual continuous observation station at a closer distance, which also provides a reference and theoretical basis for the implementation of mobile GNSS observations and the correction of observation data.
, Available online ,
doi: 10.13203/j.whugis20200226
Abstract:
Based on the method of time differenced carrier phase, the mathematical model of multi-GNSS velocity estimation is introduced and the error sources are analyzed. Using the real data, the accuracy and performance of velocity determination at each GNSS frequency point and multi-GNSS are tested and analyzed. The results show that the accuracy of velocity determination at different frequency is different, the velocity estimation at B1I, B1C, B3I frequency points in BDS and E1, E5a, E6, E5b, E5 frequency points in Galileo have the same precision, with the horizontal direction better than 1.5 mm/s and the vertical direction better than 3 mm/s; the velocity estimation accuracy at B2I frequency in BDS is the same as that at L1, L2 and L5 frequency points in GPS with the horizontal accuracy of 1.5-2 mm/s and the vertical accuracy of 3-4 mm/s; the velocity estimation accuracy at G1 and G2 frequency in GLONASS is the worst, with 3-4 mm/s in horizontal direction and 5-5.5 mm/s in vertical direction. The accuracy of double frequency ionospheric free combination is lower than that of single frequency due to the amplification of the observation noise. In addition, the combination of multiple GNSS increases the number of visible satellites, reduces the PDOP value, and can significantly improve the velocity measurement accuracy. Compared with the single GPS system, the accuracy of GPS/BDS/GLONASS/Galileo velocity estimation is improved by 40% in horizontal direction and 46% in vertical direction. At 40° elevation cut-off, the availability rate is improved from 48% to 98% under the condition of the horizontal velocity better than 1 cm/s and the vertical direction better than 2 cm/s.
Based on the method of time differenced carrier phase, the mathematical model of multi-GNSS velocity estimation is introduced and the error sources are analyzed. Using the real data, the accuracy and performance of velocity determination at each GNSS frequency point and multi-GNSS are tested and analyzed. The results show that the accuracy of velocity determination at different frequency is different, the velocity estimation at B1I, B1C, B3I frequency points in BDS and E1, E5a, E6, E5b, E5 frequency points in Galileo have the same precision, with the horizontal direction better than 1.5 mm/s and the vertical direction better than 3 mm/s; the velocity estimation accuracy at B2I frequency in BDS is the same as that at L1, L2 and L5 frequency points in GPS with the horizontal accuracy of 1.5-2 mm/s and the vertical accuracy of 3-4 mm/s; the velocity estimation accuracy at G1 and G2 frequency in GLONASS is the worst, with 3-4 mm/s in horizontal direction and 5-5.5 mm/s in vertical direction. The accuracy of double frequency ionospheric free combination is lower than that of single frequency due to the amplification of the observation noise. In addition, the combination of multiple GNSS increases the number of visible satellites, reduces the PDOP value, and can significantly improve the velocity measurement accuracy. Compared with the single GPS system, the accuracy of GPS/BDS/GLONASS/Galileo velocity estimation is improved by 40% in horizontal direction and 46% in vertical direction. At 40° elevation cut-off, the availability rate is improved from 48% to 98% under the condition of the horizontal velocity better than 1 cm/s and the vertical direction better than 2 cm/s.
, Available online ,
doi: 10.13203/j.whugis20210057
Abstract:
It is important to know the pattern of Pakistan's regional development for the construction of the China-Pakistan Economic Corridor. In this paper, we used the Visible Infrared Imager Radiometer Suite (VⅡRS) night-time light remote sensing image as the main data source, and implemented the Savitzky-Golay (SG) filtering algorithm to repair the missing values of the images. Three methods, Night-time Light Change Ratio (NLCR), Mann-Kendall (MK) trend test method, and rank-scale law, were carried out to study the nighttime light change patterns of Pakistan's provincial/city level, geographic grid level, and East-West zone in three geographic scales. The analysis of the NLCR shows that from 2012 to 2019, the entire Pakistan has night-time light growth by 47.96%; all provincial regions have different degrees of development, and with NLCR increasing by >15%; the NLCR of Gwadar Port is 126.94%, which is the highest among the 15 node cities on the east and west lines of the China-Pakistan Economic Corridor; more importantly, not all 15 node cities have similar positive night-time light growth, with two cities having night-time light decrease. This indicates that the development of the 15 node cities on the east and west lines of the China-Pakistan Economic Corridor was unbalanced. Using the MK trend test, Punjab has the most spatial grids (1905) with the total night-time light upward trend at the 5 km geographic grid-scale, while Sindh has the most grids (338) with the total night-time light downward trend. Through visual inspection of the grids with the total night-time light downward trend in Sindh, it is found that most of the grids are located in rural areas of Sindh, which is consistent to the fact that there is a serious power shortage problem in rural areas of Sindh. The rank-size analysis results show that the distribution of urban size across the country has undergone a process from a relatively concentrated distribution to a more balanced distribution; the evolution of urban size distribution in the western region is also consistent to the national pattern; while the distribution of urban size in the eastern region has shifted from balanced to more balanced development; the Pareto indexes of both the eastern zone and the western zone are gradually decreased, indicating that the gap between the urban development in Pakistan is reduced, and the entire country is developing and becoming more balanced. In addition, It is worth noting that Pakistan's frequent large-scale power outages and lack of stable power supply may be the factors explaining the low brightness of night-time light in some areas. This study reveals Pakistan's regional development patterns under long-term time series and multiple spatial scales, which can provide a certain scientific basis for the planning of the China-Pakistan Economic Corridor.
It is important to know the pattern of Pakistan's regional development for the construction of the China-Pakistan Economic Corridor. In this paper, we used the Visible Infrared Imager Radiometer Suite (VⅡRS) night-time light remote sensing image as the main data source, and implemented the Savitzky-Golay (SG) filtering algorithm to repair the missing values of the images. Three methods, Night-time Light Change Ratio (NLCR), Mann-Kendall (MK) trend test method, and rank-scale law, were carried out to study the nighttime light change patterns of Pakistan's provincial/city level, geographic grid level, and East-West zone in three geographic scales. The analysis of the NLCR shows that from 2012 to 2019, the entire Pakistan has night-time light growth by 47.96%; all provincial regions have different degrees of development, and with NLCR increasing by >15%; the NLCR of Gwadar Port is 126.94%, which is the highest among the 15 node cities on the east and west lines of the China-Pakistan Economic Corridor; more importantly, not all 15 node cities have similar positive night-time light growth, with two cities having night-time light decrease. This indicates that the development of the 15 node cities on the east and west lines of the China-Pakistan Economic Corridor was unbalanced. Using the MK trend test, Punjab has the most spatial grids (1905) with the total night-time light upward trend at the 5 km geographic grid-scale, while Sindh has the most grids (338) with the total night-time light downward trend. Through visual inspection of the grids with the total night-time light downward trend in Sindh, it is found that most of the grids are located in rural areas of Sindh, which is consistent to the fact that there is a serious power shortage problem in rural areas of Sindh. The rank-size analysis results show that the distribution of urban size across the country has undergone a process from a relatively concentrated distribution to a more balanced distribution; the evolution of urban size distribution in the western region is also consistent to the national pattern; while the distribution of urban size in the eastern region has shifted from balanced to more balanced development; the Pareto indexes of both the eastern zone and the western zone are gradually decreased, indicating that the gap between the urban development in Pakistan is reduced, and the entire country is developing and becoming more balanced. In addition, It is worth noting that Pakistan's frequent large-scale power outages and lack of stable power supply may be the factors explaining the low brightness of night-time light in some areas. This study reveals Pakistan's regional development patterns under long-term time series and multiple spatial scales, which can provide a certain scientific basis for the planning of the China-Pakistan Economic Corridor.
, Available online ,
doi: 10.13203/j.whugis20200177
Abstract:
Spatial co-location pattern discovery aims to mining subsets of geographic features which are frequently located in close proximity. Due to spatial heterogeneity, the frequency of different features being co-located usually varies across the space, thus forming the regional co-location patterns. Currently, most methods for discovering regional co-location patterns focus on planar geospatial spaces which can hardly support the corresponding analysis on network space such as urban roads. Therefore, this paper proposes a regional network co-location pattern mining method based on spatial scan statistics. First, a network-constrained path expansion method is developed to detect the candidate paths where co-location patterns could occur. These candidates are further validated using a significance test, where the null model is constructed using a network-constrained bivariate Poisson distribution. Experiments using simulated data and taxi datasets show that the proposed method is more effective for discovering regional co-location patterns on the network space than a baseline method.
Spatial co-location pattern discovery aims to mining subsets of geographic features which are frequently located in close proximity. Due to spatial heterogeneity, the frequency of different features being co-located usually varies across the space, thus forming the regional co-location patterns. Currently, most methods for discovering regional co-location patterns focus on planar geospatial spaces which can hardly support the corresponding analysis on network space such as urban roads. Therefore, this paper proposes a regional network co-location pattern mining method based on spatial scan statistics. First, a network-constrained path expansion method is developed to detect the candidate paths where co-location patterns could occur. These candidates are further validated using a significance test, where the null model is constructed using a network-constrained bivariate Poisson distribution. Experiments using simulated data and taxi datasets show that the proposed method is more effective for discovering regional co-location patterns on the network space than a baseline method.
, Available online ,
doi: 10.13203/j.whugis20200345
Abstract:
To determine the geologic age of the lunar surface is the foundation of the research on the formation and evolution of the Moon, and of the inversion of the processes of lunar geological events. Lack of lunar rock and soil samples limits the range of geological units that can used for isotopic dating. Therefore, the dating using crater size-frequency distribution (CSFD) is employed to obtain the geologic ages of broader regions on the lunar surface. However, the presence of secondary craters will lead to a deviation in geologic age which is obtained by CSFD method. Thus, to get a more accurate geologic age, secondary craters should be eliminated. This paper presents a method to identify secondary craters based on the distribution of iron element on lunar surface. First, the method assumes that the iron content in secondary craters is close to that in lunar regolith. Then, band ratio method is utilized to acquire the iron content. Finally, secondary craters are distinguished from primary ones in terms of the difference of iron content between craters and lunar regolith. The effectiveness and robustness of the proposed method were tested using MI multispectral data of the Japanese Selene Mission. The experimental results show that the geologic age deviation compared with the known isotopic dated ones is less than 0.04Ga, which shows good consistency. Compared with other secondary craters identification methods, the proposed approach is proven to be more effective and robust.
To determine the geologic age of the lunar surface is the foundation of the research on the formation and evolution of the Moon, and of the inversion of the processes of lunar geological events. Lack of lunar rock and soil samples limits the range of geological units that can used for isotopic dating. Therefore, the dating using crater size-frequency distribution (CSFD) is employed to obtain the geologic ages of broader regions on the lunar surface. However, the presence of secondary craters will lead to a deviation in geologic age which is obtained by CSFD method. Thus, to get a more accurate geologic age, secondary craters should be eliminated. This paper presents a method to identify secondary craters based on the distribution of iron element on lunar surface. First, the method assumes that the iron content in secondary craters is close to that in lunar regolith. Then, band ratio method is utilized to acquire the iron content. Finally, secondary craters are distinguished from primary ones in terms of the difference of iron content between craters and lunar regolith. The effectiveness and robustness of the proposed method were tested using MI multispectral data of the Japanese Selene Mission. The experimental results show that the geologic age deviation compared with the known isotopic dated ones is less than 0.04Ga, which shows good consistency. Compared with other secondary craters identification methods, the proposed approach is proven to be more effective and robust.
, Available online ,
doi: 10.13203/j.whugis20200571
Abstract:
Objectives: To solve the problems of high cost and high complexity of the traditional estimation method of the moving vehicles attitude angle, a innovative vehicle heading and pitch estimation model based on optimized time-differenced carrier phase is proposed. Methods: Firstly, this model only uses the observation data of one GNSS receiver to obtain accurate vehicle displacement vector using a low-complexity algorithm, time-differenced carrier phase. Then, the vehicle heading and angle are estimated using accurate displacement vector. Among them, in order to improve the estimation efficiency, the model optimizes the traditional time-differenced carrier phase algorithm. Results: Static and dynamic tests show that:(1) The optimized time-differenced carrier phase is more efficient than the general carrier phase time-difference, and the processing time can be saved by about 40%. (2) The proposed heading and pitch estimation model can provide accurate heading and pitch, whose root mean square error is less than 0.2ånd maximum error is less than 1.5°. (3) The accuracy of the model will not be affected by the accumulation of errors within one hour. Conclusions: This model uses only one receiver to obtain heading and pitch of moving vehicles, which has the advantages of high accuracy, low cost, low complexity and high efficiency.
Objectives: To solve the problems of high cost and high complexity of the traditional estimation method of the moving vehicles attitude angle, a innovative vehicle heading and pitch estimation model based on optimized time-differenced carrier phase is proposed. Methods: Firstly, this model only uses the observation data of one GNSS receiver to obtain accurate vehicle displacement vector using a low-complexity algorithm, time-differenced carrier phase. Then, the vehicle heading and angle are estimated using accurate displacement vector. Among them, in order to improve the estimation efficiency, the model optimizes the traditional time-differenced carrier phase algorithm. Results: Static and dynamic tests show that:(1) The optimized time-differenced carrier phase is more efficient than the general carrier phase time-difference, and the processing time can be saved by about 40%. (2) The proposed heading and pitch estimation model can provide accurate heading and pitch, whose root mean square error is less than 0.2ånd maximum error is less than 1.5°. (3) The accuracy of the model will not be affected by the accumulation of errors within one hour. Conclusions: This model uses only one receiver to obtain heading and pitch of moving vehicles, which has the advantages of high accuracy, low cost, low complexity and high efficiency.
, Available online ,
doi: 10.13203/j.whugis20200195
Abstract:
Based on the first asteroid exploration plan announced by China Space Administration on April 19, 2019, we develop an optical orbit determination software for the main belt comet 133P/Elst-Pizarro (7968), which is one of the mission targets. The 133P/Elst-Pizarro's ground-based optical observation data from July 24, 1979 to October 28, 2019 are analyzed. Compared with the well-known OrbFit software system, it is found that the residual distribution is consistent, the measurement statistical residual RMS is less than 0.01″, and the internal coincidence accuracy of orbit determination is also consistent with each other. The results suggests the reliability of our software. Furthermore, we carry out a simulation orbit determination analysis aimed at 133P/Elst-Pizarro to discuss the orbit determination accuracy from ground-based optical data. When we use 20-year optical observation data measured once a mouth from Yunnan and Chile station and adding Gaussian white noise which is close to the current actual observation level, the results reflect that the optical orbit determination accuracy of the asteroid is at 50 km level. At the same time, it also shows that the optical orbit determination accuracy of the asteroid can be effectively improved by increasing the observation data or reducing the observation noise.
Based on the first asteroid exploration plan announced by China Space Administration on April 19, 2019, we develop an optical orbit determination software for the main belt comet 133P/Elst-Pizarro (7968), which is one of the mission targets. The 133P/Elst-Pizarro's ground-based optical observation data from July 24, 1979 to October 28, 2019 are analyzed. Compared with the well-known OrbFit software system, it is found that the residual distribution is consistent, the measurement statistical residual RMS is less than 0.01″, and the internal coincidence accuracy of orbit determination is also consistent with each other. The results suggests the reliability of our software. Furthermore, we carry out a simulation orbit determination analysis aimed at 133P/Elst-Pizarro to discuss the orbit determination accuracy from ground-based optical data. When we use 20-year optical observation data measured once a mouth from Yunnan and Chile station and adding Gaussian white noise which is close to the current actual observation level, the results reflect that the optical orbit determination accuracy of the asteroid is at 50 km level. At the same time, it also shows that the optical orbit determination accuracy of the asteroid can be effectively improved by increasing the observation data or reducing the observation noise.
, Available online ,
doi: 10.13203/j.whugis20200029
Abstract:
Objectives: Because the signal control with fixed phase duration can not be adjusted adaptively according to the real-time road conditions, the improvement of traffic congestion phenomenon is limited. In order to simulate the phase adaptation of traffic lights according to the real-time road conditions, reasonably configure the phase duration of traffic lights and alleviate the traffic congestion, a closed-loop system of traffic lights timing optimization and self-feedback adjustment which include vehicles flow video acquisition, flow statistics, timing calculation, timing feedback, phase regulation, flow statistics and timing optimization was constructed. Methods: Built on Webster algorithm, combining virtual simulation and computer vision technology, a road traffic simulation scene based on Unity3D is constructed to simulate vehicle starting and stopping, driving and signal lights control. Then, the OpenCV library is used to process the traffic flow video captured by Camera Object in the scene, count the traffic flow, calculate the optimal signal cycle, and determine the traffic phase. Finally, the result of timing calculation is applicable to the simulation scene, and further timing optimization is implemented according to the real-time scene to realize the signal phase adjustment and optimization cycle. Results: Traffic flow generation is built on the number of vehicles arriving at the intersection during peak hours. Vehicles are randomly generated in each direction of intersection every minute, including 15-30 vehicles in congestion direction and 5-15 vehicles in non-congestion direction. The initial setting of intersection signal lights is the traditional fixed timing schema, in which the left turn green light is on for 20 seconds in the north-south direction, the yellow light is on for 3 seconds, the straight green light is 30 seconds, and the red light is on for 56 seconds; the straight red light is on for 56 seconds, the left turn green light is on for 20 seconds in the east-west direction, the yellow light is on for 3 seconds, and the straight green light is on for 30 seconds. Report to experimental results of the fixed timing scheme, (1) the average waiting time of intersection vehicles has decreased by 17 seconds; (2) the traffic volume has increased by 190 vehicles per hour with real-time timing optimization scheme. Conclusions: From the results of simulation experiments, the simulation of traffic signal timing using unity3d provides a very effective method for the verification of traffic signal timing algorithm. The closed-loop system of signal lights adaptive timing optimization makes up for the limitations of existing traffic lights timing optimization schemes, and achieves real-time timing optimization. Simulation results show that the closed-loop feedback of signal lamp adaptive timing optimization can greatly reduce the waiting time of vehicles and effectively alleviate traffic congestion.
Objectives: Because the signal control with fixed phase duration can not be adjusted adaptively according to the real-time road conditions, the improvement of traffic congestion phenomenon is limited. In order to simulate the phase adaptation of traffic lights according to the real-time road conditions, reasonably configure the phase duration of traffic lights and alleviate the traffic congestion, a closed-loop system of traffic lights timing optimization and self-feedback adjustment which include vehicles flow video acquisition, flow statistics, timing calculation, timing feedback, phase regulation, flow statistics and timing optimization was constructed. Methods: Built on Webster algorithm, combining virtual simulation and computer vision technology, a road traffic simulation scene based on Unity3D is constructed to simulate vehicle starting and stopping, driving and signal lights control. Then, the OpenCV library is used to process the traffic flow video captured by Camera Object in the scene, count the traffic flow, calculate the optimal signal cycle, and determine the traffic phase. Finally, the result of timing calculation is applicable to the simulation scene, and further timing optimization is implemented according to the real-time scene to realize the signal phase adjustment and optimization cycle. Results: Traffic flow generation is built on the number of vehicles arriving at the intersection during peak hours. Vehicles are randomly generated in each direction of intersection every minute, including 15-30 vehicles in congestion direction and 5-15 vehicles in non-congestion direction. The initial setting of intersection signal lights is the traditional fixed timing schema, in which the left turn green light is on for 20 seconds in the north-south direction, the yellow light is on for 3 seconds, the straight green light is 30 seconds, and the red light is on for 56 seconds; the straight red light is on for 56 seconds, the left turn green light is on for 20 seconds in the east-west direction, the yellow light is on for 3 seconds, and the straight green light is on for 30 seconds. Report to experimental results of the fixed timing scheme, (1) the average waiting time of intersection vehicles has decreased by 17 seconds; (2) the traffic volume has increased by 190 vehicles per hour with real-time timing optimization scheme. Conclusions: From the results of simulation experiments, the simulation of traffic signal timing using unity3d provides a very effective method for the verification of traffic signal timing algorithm. The closed-loop system of signal lights adaptive timing optimization makes up for the limitations of existing traffic lights timing optimization schemes, and achieves real-time timing optimization. Simulation results show that the closed-loop feedback of signal lamp adaptive timing optimization can greatly reduce the waiting time of vehicles and effectively alleviate traffic congestion.
, Available online ,
doi: 10.13203/j.whugis20200008
Abstract:
The deep learning model such as convolutional neural network has achieved satisfactory results in hyperspectral images classification. However, because traditional neurons can only perform scalar computation, the existing deep learning models cannot model the instantiation parameters of hyperspectral image features, so they cannot achieve satisfactory classification results under the condition of restricted neighborhood scope. To solve the problem, this paper designs a new network model by introducing capsule network structure. This model can further improve the classification accuracy of hyperspectral images by using the vector calculation of capsule neurons and the spatial relationship between features encoded by the weight matrix. Through verification on Pavia University, Indian Pines and Salinas data sets, experiments show that the network model proposed in this paper has better classification performance than the traditional algorithm and the convolutional neural network model, and has better adaptability to the training sample number and pixel neighborhood scope.
The deep learning model such as convolutional neural network has achieved satisfactory results in hyperspectral images classification. However, because traditional neurons can only perform scalar computation, the existing deep learning models cannot model the instantiation parameters of hyperspectral image features, so they cannot achieve satisfactory classification results under the condition of restricted neighborhood scope. To solve the problem, this paper designs a new network model by introducing capsule network structure. This model can further improve the classification accuracy of hyperspectral images by using the vector calculation of capsule neurons and the spatial relationship between features encoded by the weight matrix. Through verification on Pavia University, Indian Pines and Salinas data sets, experiments show that the network model proposed in this paper has better classification performance than the traditional algorithm and the convolutional neural network model, and has better adaptability to the training sample number and pixel neighborhood scope.
, Available online ,
doi: 10.13203/j.whugis20200386
Abstract:
The pollution of heavy metal has become increasingly serious in recent years. The accumulation of heavy metals in the soil will be a threat to ecological balance and human health. Therefore, we need to obtain heavy metal content in soil quickly and accurately. With the development of Hyperspectral technique, it makes it possible to estimate heavy metal content fast and at low cost by spectral data. Because the field spectra tends to be affected by enviro nmental factors, and the number of samples is insufficient in existing studies, this paper proposed a method of constructing model by combining field spectra with laboratory spectra. First of all, Direct Standardization algorithm (DS) was employed to eliminate the environmental factors of the field spectra. Secondly, laboratory spectra was introduced to joint modeling to enhance the diversity of the samples. Finally, the characteristic spectra of iron oxide were extracted for modeling to increase the rationality of model. This method was validated by the spectral data of 70 soil samples from Xiongan farming area in Hebei province. The model established by field spectra without DS correction and characteristic spectrum obtained an accuracy (R2) of only 0.22. However, the R2 of the proposed method in this paper reached 0.91, and the model had excellent estimation ability. It indicates that spectral modeling of Pb content can be significantly improved by removing the influence of environmental factors on the field spectra, extracting the iron oxide characteristic spectra of the combined field and laboratory spectra.
The pollution of heavy metal has become increasingly serious in recent years. The accumulation of heavy metals in the soil will be a threat to ecological balance and human health. Therefore, we need to obtain heavy metal content in soil quickly and accurately. With the development of Hyperspectral technique, it makes it possible to estimate heavy metal content fast and at low cost by spectral data. Because the field spectra tends to be affected by enviro nmental factors, and the number of samples is insufficient in existing studies, this paper proposed a method of constructing model by combining field spectra with laboratory spectra. First of all, Direct Standardization algorithm (DS) was employed to eliminate the environmental factors of the field spectra. Secondly, laboratory spectra was introduced to joint modeling to enhance the diversity of the samples. Finally, the characteristic spectra of iron oxide were extracted for modeling to increase the rationality of model. This method was validated by the spectral data of 70 soil samples from Xiongan farming area in Hebei province. The model established by field spectra without DS correction and characteristic spectrum obtained an accuracy (R2) of only 0.22. However, the R2 of the proposed method in this paper reached 0.91, and the model had excellent estimation ability. It indicates that spectral modeling of Pb content can be significantly improved by removing the influence of environmental factors on the field spectra, extracting the iron oxide characteristic spectra of the combined field and laboratory spectra.
, Available online ,
doi: 10.13203/j.whugis20210286
Abstract:
Objectives: The performance of the GNSS spaceborne atomic clock affects the entire navigation system, by having direct impact on GNSS measurement quality, ranging precision, clock prediction and satellite autonomous navigation capabilities. Different time Synchronization systems may have different impact on the evaluation of spaceborne atomic clock. Precise clock bias data determined by inter-satellite link (ISL), two-way time transfer (TWTT) and orbit determination and time synchronization (ODTS) system are used to further evaluate the performance of Beidou-3 on-orbit atomic clock. Methods: Quadratic polynomial model and total Allan/Hadamard variance are used to analyze the performance of the Beidou-3 satellite clock data of the three different time synchronization systems. Results: The result shows that the frequency accuracy and frequency drift of the above three clock bias determination systems are consistent. The frequency accuracy of all satellites is within the range of (-4~2)×10-11. Frequency accuracy of hydrogen clock is better than that of rubidium clock. The frequency drift based on the ISL system is slightly better than that of the ODTS; the three clock determination systems have their own advantages in evaluating the stability of the atomic clock, respectively. For short-term stability, the ODTS whose 3000s stability reaches 3×10-14 is better than that of ISL, and hydrogen clock is better than rubidium clock. For medium-term and long-term stability, when the averaging time is greater than 1×104 s, the result of ISL is closer to the actual condition of the Beidou-3 spaceborne clock; for long-term stability more than 7 days, the broadcast ephemeris bias based on TWTT system can be used for rapid evaluation, which result is close to ODTS and ISL. Conclusions: The three clock bias determination systems can be used to evaluate the frequency accuracy and frequency drift of on-orbit atomic clocks with basically consistent statistical results. The three clock bias determination systems have their own advantages in assessing the stability of the orbiting satellite clock when selecting different averaging time.
Objectives: The performance of the GNSS spaceborne atomic clock affects the entire navigation system, by having direct impact on GNSS measurement quality, ranging precision, clock prediction and satellite autonomous navigation capabilities. Different time Synchronization systems may have different impact on the evaluation of spaceborne atomic clock. Precise clock bias data determined by inter-satellite link (ISL), two-way time transfer (TWTT) and orbit determination and time synchronization (ODTS) system are used to further evaluate the performance of Beidou-3 on-orbit atomic clock. Methods: Quadratic polynomial model and total Allan/Hadamard variance are used to analyze the performance of the Beidou-3 satellite clock data of the three different time synchronization systems. Results: The result shows that the frequency accuracy and frequency drift of the above three clock bias determination systems are consistent. The frequency accuracy of all satellites is within the range of (-4~2)×10-11. Frequency accuracy of hydrogen clock is better than that of rubidium clock. The frequency drift based on the ISL system is slightly better than that of the ODTS; the three clock determination systems have their own advantages in evaluating the stability of the atomic clock, respectively. For short-term stability, the ODTS whose 3000s stability reaches 3×10-14 is better than that of ISL, and hydrogen clock is better than rubidium clock. For medium-term and long-term stability, when the averaging time is greater than 1×104 s, the result of ISL is closer to the actual condition of the Beidou-3 spaceborne clock; for long-term stability more than 7 days, the broadcast ephemeris bias based on TWTT system can be used for rapid evaluation, which result is close to ODTS and ISL. Conclusions: The three clock bias determination systems can be used to evaluate the frequency accuracy and frequency drift of on-orbit atomic clocks with basically consistent statistical results. The three clock bias determination systems have their own advantages in assessing the stability of the orbiting satellite clock when selecting different averaging time.
, Available online ,
doi: 10.13203/j.whugis20210198
Abstract:
The global constellation networking of BeiDou Navigation Satellite System (BDS) has been completed, which means that the BDS-3 has entered a new era of providing high-quality positioning, navigation and timing service for global users. To comprehensively compare the performance of BDS-3 uncombined precise point positioning (PPP) with other global navigation satellite systems (GNSS), three aspects are focused:the consistency of BDS-3 precise orbit and clock products among different analysis centers, the satellite availability of BDS-3/GNSS, the positioning performance of BDS-3/GNSS single-system and multi-system PPP. Based on the precise orbit and clock products from five analysis centers, the three-dimensional root mean square error of BDS-3 static PPP is about 2.31 cm~4.00 cm, and its convergence time is significantly slower than that of other GNSS. The introduction of GPS observations can achieve the most obvious improvement among BDS-3/GNSS dual-system joint PPP. Besides, the quad-constellation observations can effectively shorten the convergence time of PPP and improve the positioning accuracy on kinematic mode.
The global constellation networking of BeiDou Navigation Satellite System (BDS) has been completed, which means that the BDS-3 has entered a new era of providing high-quality positioning, navigation and timing service for global users. To comprehensively compare the performance of BDS-3 uncombined precise point positioning (PPP) with other global navigation satellite systems (GNSS), three aspects are focused:the consistency of BDS-3 precise orbit and clock products among different analysis centers, the satellite availability of BDS-3/GNSS, the positioning performance of BDS-3/GNSS single-system and multi-system PPP. Based on the precise orbit and clock products from five analysis centers, the three-dimensional root mean square error of BDS-3 static PPP is about 2.31 cm~4.00 cm, and its convergence time is significantly slower than that of other GNSS. The introduction of GPS observations can achieve the most obvious improvement among BDS-3/GNSS dual-system joint PPP. Besides, the quad-constellation observations can effectively shorten the convergence time of PPP and improve the positioning accuracy on kinematic mode.
, Available online ,
doi: 10.13203/j.whugis20210133
Abstract:
Objectives: In the large-scale of virtual reality scene, it is difficult to add all graphics data into the video memory for rendering. Removing the occluded objects in advance by visible query technology can reduce the amount of data loaded on the display end to improve the rendering efficiency. Therefore, the research of visible query method for regional objects has important application value for real-time rendering of large-scale urban scene. Methods: In this paper, we put forward a distributed visible query method based on Map-Reduce. In the map phase, we apply a hierarchical axis-aligned bounding box as viewpoint space partition. When the number of 3D objects in viewpoint space partition exceeds the threshold, the axis-aligned bounding box continues to be divided into sub-boxes. After the above process, the map tasks produce GeoTuples with the VSPID as key and visible query candidate set as value. In the reduce phase, a viewpoint is created for each leaf axis-aligned bounding box where the binary space partitioning trees are build and the visible set is calculated using real-time occlusion algorithm. Results: The study experimented with a building compound, containing more than 200,000 geometric solids, in Shenzhen, China. The experimental results show that:(1) There is no simple linear relationship between the running time of distributed visible query and the number of viewpoint space partitions. (2) Running time and parallelism are not simply inversely proportional. The computational efficiency of each process first increases and then decreases with the increase of parallelism. About 48 parallelism, the process has the highest efficiency. (3) Whether the distributed approach is better than the traditional approach depends on the number of 3D objects. After the amount of 3D objects reaches about 40,000, the distributed algorithm begins to be better than the traditional algorithm. Conclusions: The computational experiments reveal the proposed algorithms outperform competitors in terms of the processing efficiency and feasibility, which can meet the requirement of visible query in large-scale scenarios.
Objectives: In the large-scale of virtual reality scene, it is difficult to add all graphics data into the video memory for rendering. Removing the occluded objects in advance by visible query technology can reduce the amount of data loaded on the display end to improve the rendering efficiency. Therefore, the research of visible query method for regional objects has important application value for real-time rendering of large-scale urban scene. Methods: In this paper, we put forward a distributed visible query method based on Map-Reduce. In the map phase, we apply a hierarchical axis-aligned bounding box as viewpoint space partition. When the number of 3D objects in viewpoint space partition exceeds the threshold, the axis-aligned bounding box continues to be divided into sub-boxes. After the above process, the map tasks produce GeoTuples with the VSPID as key and visible query candidate set as value. In the reduce phase, a viewpoint is created for each leaf axis-aligned bounding box where the binary space partitioning trees are build and the visible set is calculated using real-time occlusion algorithm. Results: The study experimented with a building compound, containing more than 200,000 geometric solids, in Shenzhen, China. The experimental results show that:(1) There is no simple linear relationship between the running time of distributed visible query and the number of viewpoint space partitions. (2) Running time and parallelism are not simply inversely proportional. The computational efficiency of each process first increases and then decreases with the increase of parallelism. About 48 parallelism, the process has the highest efficiency. (3) Whether the distributed approach is better than the traditional approach depends on the number of 3D objects. After the amount of 3D objects reaches about 40,000, the distributed algorithm begins to be better than the traditional algorithm. Conclusions: The computational experiments reveal the proposed algorithms outperform competitors in terms of the processing efficiency and feasibility, which can meet the requirement of visible query in large-scale scenarios.
, Available online ,
doi: 10.13203/j.whugis20200001
Abstract:
To deal with the high computation expense in data-intensive heat map generation, parallel computing technology based on Graphics Processing Unit(GPU) is introduced to the heat map generation filed. With combination of trajectory line model, we propose a novel method for trajectory line heat map generation and display based on GPU. First of all, in order to avoid discontinuity and inhomogeneity of heat value resulted from different dense of trajectory points or unreasonable neighborhood threshold, trajectory line model is adopted to improve the effect of generated heat map. Second, aiming at the problem of low efficiency of heat map generation caused by large-scale data calculation, the proposed method improve the efficiency greatly via a combination of GPU parallel computing, tuning of kernel function parameter, loop unrolling and Pixel Buffer Object(PBO). Experiment results show that processing speed of the proposed method is from 5 to 30 times faster than CPU-based implementation. Additionally, the accelerator ratio tends to be higher as the resolution of heat map or the increase of trajectory data.
To deal with the high computation expense in data-intensive heat map generation, parallel computing technology based on Graphics Processing Unit(GPU) is introduced to the heat map generation filed. With combination of trajectory line model, we propose a novel method for trajectory line heat map generation and display based on GPU. First of all, in order to avoid discontinuity and inhomogeneity of heat value resulted from different dense of trajectory points or unreasonable neighborhood threshold, trajectory line model is adopted to improve the effect of generated heat map. Second, aiming at the problem of low efficiency of heat map generation caused by large-scale data calculation, the proposed method improve the efficiency greatly via a combination of GPU parallel computing, tuning of kernel function parameter, loop unrolling and Pixel Buffer Object(PBO). Experiment results show that processing speed of the proposed method is from 5 to 30 times faster than CPU-based implementation. Additionally, the accelerator ratio tends to be higher as the resolution of heat map or the increase of trajectory data.
, Available online ,
doi: 10.13203/j.whugis20210237
Abstract:
Objectives: Rockfall occur frequently in the mountainous areas of southwest China, which is easy to cause huge casualties and property losses. A rapid simulation method of the collapse process is urgently needed. At present, the commonly used collapse simulation software still has obvious shortcomings, such as low terrain accuracy, failure to consider the structural characteristics of rock mass, failure to realize block collision and fragmentation, etc., and the logic operation based on Central Processing Unit (CPU) only limits the calculation speed. In this paper, a new method for rapid simulation of the three-dimensional motion process of collapse is proposed. Methods: The Unmanned aerial vehicle (UAV) aerial photography modelling combined with field investigation is used to obtain the slope surface model and determine the characteristic parameters of rockfall. The simulation software of large-scale collapse movement process is developed by using Unity3D platform which integrates PhysX physics engine and Central Processing Unit-Graphics Processing Unit (CPU-GPU) parallel computing capability. Results: The software can reproduce the whole process of collapse-impact-fragmentation-accumulation; It can output the three-dimensional trajectory, velocity, energy and jumping height of collapse, which provides reliable basis for the design of collapse prevention and control. Finally, taking Xiejiayan collapse in Nayong, Guizhou Province as a prototype case, three-dimensional simulation and verification of the collapse movement process are carried out. The simulation results of the rockfall accumulation range are in good agreement with the field investigation of the rockfall accumulation range at the bottom of the slope, and the movement characteristics of the simulated single block rock conform to the real physical laws, which indicates the feasibility and practicability of this method. Conclusions: This method can solve the problem of simulation, analysis and visualization of the whole process of three-dimensional movement of rockfall.
Objectives: Rockfall occur frequently in the mountainous areas of southwest China, which is easy to cause huge casualties and property losses. A rapid simulation method of the collapse process is urgently needed. At present, the commonly used collapse simulation software still has obvious shortcomings, such as low terrain accuracy, failure to consider the structural characteristics of rock mass, failure to realize block collision and fragmentation, etc., and the logic operation based on Central Processing Unit (CPU) only limits the calculation speed. In this paper, a new method for rapid simulation of the three-dimensional motion process of collapse is proposed. Methods: The Unmanned aerial vehicle (UAV) aerial photography modelling combined with field investigation is used to obtain the slope surface model and determine the characteristic parameters of rockfall. The simulation software of large-scale collapse movement process is developed by using Unity3D platform which integrates PhysX physics engine and Central Processing Unit-Graphics Processing Unit (CPU-GPU) parallel computing capability. Results: The software can reproduce the whole process of collapse-impact-fragmentation-accumulation; It can output the three-dimensional trajectory, velocity, energy and jumping height of collapse, which provides reliable basis for the design of collapse prevention and control. Finally, taking Xiejiayan collapse in Nayong, Guizhou Province as a prototype case, three-dimensional simulation and verification of the collapse movement process are carried out. The simulation results of the rockfall accumulation range are in good agreement with the field investigation of the rockfall accumulation range at the bottom of the slope, and the movement characteristics of the simulated single block rock conform to the real physical laws, which indicates the feasibility and practicability of this method. Conclusions: This method can solve the problem of simulation, analysis and visualization of the whole process of three-dimensional movement of rockfall.
, Available online ,
doi: 10.13203/j.whugis20200508
Abstract:
Machine learning language has become an ideal modeling tool in the field of dam health monitoring with its powerful nonlinear data mining ability. However, the minimum fitting mean square error (MSE) is determined as the only optimization objective in the traditional modeling process, which is likely to cause over-fitting problems. To overcome this problem, based on the relevance vector machine (RVM), a probabilistic prediction model is established under the constraint of double optimization objectives, which integrates the deformation spatial association and MSE. The deformation spatial association is quantified by the shape similarity index (SSI) at first. The double objective is then established with the combination of the MSE and SSI, and is achieved by making the MSE as small as possible, while the SSI is as large as possible. Engineering example of the Jinping-I arch dam shows that the average decrease proportion of the root mean square error (RMSE) and maximum absolute error (ME) of the proposed double objective RVM model is 31.2% and 24.8%, respectively, and the prediction performance can be further improved by using the multi-kernel function. The prediction confidence bandwidth of the RVM model is significantly smaller than that of the traditional multiple linear regression model, with an average decrease proportion of 75.1%. Therefore, the multi-kernel double objective RVM model established for the displacement of super high arch dams can effectively improve the prediction performance and reduce the uncertainty.
Machine learning language has become an ideal modeling tool in the field of dam health monitoring with its powerful nonlinear data mining ability. However, the minimum fitting mean square error (MSE) is determined as the only optimization objective in the traditional modeling process, which is likely to cause over-fitting problems. To overcome this problem, based on the relevance vector machine (RVM), a probabilistic prediction model is established under the constraint of double optimization objectives, which integrates the deformation spatial association and MSE. The deformation spatial association is quantified by the shape similarity index (SSI) at first. The double objective is then established with the combination of the MSE and SSI, and is achieved by making the MSE as small as possible, while the SSI is as large as possible. Engineering example of the Jinping-I arch dam shows that the average decrease proportion of the root mean square error (RMSE) and maximum absolute error (ME) of the proposed double objective RVM model is 31.2% and 24.8%, respectively, and the prediction performance can be further improved by using the multi-kernel function. The prediction confidence bandwidth of the RVM model is significantly smaller than that of the traditional multiple linear regression model, with an average decrease proportion of 75.1%. Therefore, the multi-kernel double objective RVM model established for the displacement of super high arch dams can effectively improve the prediction performance and reduce the uncertainty.
, Available online ,
doi: 10.13203/j.whugis20210209
Abstract:
Objectives: In order to explore the impact of the decrease of human activities on the air quality in China during the period of the Corona Virus Disease 2019 (COVID-19), the temporal and spatial abnormal changes of Aerosol Optical Depth (AOD), Precipitable Water Vapor (PWV) and Temperature (T) were analyzed, and the impact of human activities on the air quality was revealed. Methods: Firstly, the accuracy of AOD, PWV and T is verified by comparing with AOD provided by AERONET and PWV and T provided by radiosonde. Then, we analyze the long-term trends of AOD, PWV and T during the weekend and the week, and find that human activities have a certain impact on the air quality. Secondly, the temporal and spatial changes of AOD, PWV and T during the period of COVID-19 were studied, which confirmed that there was a good correlation between human activities and air quality. Finally, 184 cities of different grades in China are selected for further analysis to determine the impact of population density on AOD, PWV and T, and further reveal the specific response relationship between human activities and air quality. Results: Through the verification of the accuracy of the data used in this paper, it is found that the data selected in this paper have high accuracy, which can be used in this experimental study. By analyzing the COVID-19 PWV, AOD and T changes, we found that PWV, AOD and T were all affected by the epidemic. Conclusions: Due to the influence of COVID-19, AOD, PWV and T show different trends. At the same time, it is found that the main reason for this phenomenon is the influence of the intensity of human activities.
Objectives: In order to explore the impact of the decrease of human activities on the air quality in China during the period of the Corona Virus Disease 2019 (COVID-19), the temporal and spatial abnormal changes of Aerosol Optical Depth (AOD), Precipitable Water Vapor (PWV) and Temperature (T) were analyzed, and the impact of human activities on the air quality was revealed. Methods: Firstly, the accuracy of AOD, PWV and T is verified by comparing with AOD provided by AERONET and PWV and T provided by radiosonde. Then, we analyze the long-term trends of AOD, PWV and T during the weekend and the week, and find that human activities have a certain impact on the air quality. Secondly, the temporal and spatial changes of AOD, PWV and T during the period of COVID-19 were studied, which confirmed that there was a good correlation between human activities and air quality. Finally, 184 cities of different grades in China are selected for further analysis to determine the impact of population density on AOD, PWV and T, and further reveal the specific response relationship between human activities and air quality. Results: Through the verification of the accuracy of the data used in this paper, it is found that the data selected in this paper have high accuracy, which can be used in this experimental study. By analyzing the COVID-19 PWV, AOD and T changes, we found that PWV, AOD and T were all affected by the epidemic. Conclusions: Due to the influence of COVID-19, AOD, PWV and T show different trends. At the same time, it is found that the main reason for this phenomenon is the influence of the intensity of human activities.
, Available online ,
doi: 10.13203/j.whugis20200186
Abstract:
To solve the problems in pedestrian dead reckoning algorithms for indoor positioning, of which the step recognition accuracy for step detection is not high enough, the synchronous control is not precise enough, and there is a large location deviation. An algorithm of improved finite state machine step detection for the activity of flat holding smartphone was proposed. A finite number of states were set to correspond to the trend of resultant acceleration variation during walking. Step detection and step cycle estimation were realized based on adjacent resultant acceleration difference and several thresholds of climbing and descending times. Experimental tests were conducted by two testers in 211 meters corridors with flat holding smartphone, respectively. Experimental results show that the accuracy of two step detection tests are both 100% by using the improved algorithm. It is 0.004 seconds earlier on average than the actual time for each step. And the average location error is 0.384m. Compared to the auto-correction analysis and acceleration differential based on finite state machine algorithms, the accuracy of step recognition, synchronous control and location estimation are improved at least 0.7%, 60% and 21.15%, respectively. The proposed algorithm behaves better than the existing algorithms in the aspects of the step recognition, the synchronous control and location estimation.
To solve the problems in pedestrian dead reckoning algorithms for indoor positioning, of which the step recognition accuracy for step detection is not high enough, the synchronous control is not precise enough, and there is a large location deviation. An algorithm of improved finite state machine step detection for the activity of flat holding smartphone was proposed. A finite number of states were set to correspond to the trend of resultant acceleration variation during walking. Step detection and step cycle estimation were realized based on adjacent resultant acceleration difference and several thresholds of climbing and descending times. Experimental tests were conducted by two testers in 211 meters corridors with flat holding smartphone, respectively. Experimental results show that the accuracy of two step detection tests are both 100% by using the improved algorithm. It is 0.004 seconds earlier on average than the actual time for each step. And the average location error is 0.384m. Compared to the auto-correction analysis and acceleration differential based on finite state machine algorithms, the accuracy of step recognition, synchronous control and location estimation are improved at least 0.7%, 60% and 21.15%, respectively. The proposed algorithm behaves better than the existing algorithms in the aspects of the step recognition, the synchronous control and location estimation.
, Available online ,
doi: 10.13203/j.whugis20200115
Abstract:
Integration of GNSS and INS can provide continuous and accurate positioning information for vehicles. However, the accuracy of low-cost GNSS/INS vehicle integrated navigation systems is unreliable during GNSS outages, which are common in urban areas. So, a long and short-term memory (LSTM) networks-aided GNSS/INS integrated navigation system based on extended Kalman filter (EKF) is proposed in this paper. LSTM networks are trained to learn the relationship between position error and INS output when GNSS available. When GNSS outage occurs, LSTM networks predict and correct errors of the integrated navigation system to improve location precision. The experiment shows that the north error and east error of the GNSS/INS integrated navigation systems based on EKF is 1.93m and 13.92m during the 15s GNSS outage. Meanwhile, the north error and east error of the GNSS/INS integrated navigation systems based on LSTM-EKF is 1.17m and 0.84m. The comparison results indicate that the proposed system can effectively improve location precision during GNSS outages.
Integration of GNSS and INS can provide continuous and accurate positioning information for vehicles. However, the accuracy of low-cost GNSS/INS vehicle integrated navigation systems is unreliable during GNSS outages, which are common in urban areas. So, a long and short-term memory (LSTM) networks-aided GNSS/INS integrated navigation system based on extended Kalman filter (EKF) is proposed in this paper. LSTM networks are trained to learn the relationship between position error and INS output when GNSS available. When GNSS outage occurs, LSTM networks predict and correct errors of the integrated navigation system to improve location precision. The experiment shows that the north error and east error of the GNSS/INS integrated navigation systems based on EKF is 1.93m and 13.92m during the 15s GNSS outage. Meanwhile, the north error and east error of the GNSS/INS integrated navigation systems based on LSTM-EKF is 1.17m and 0.84m. The comparison results indicate that the proposed system can effectively improve location precision during GNSS outages.
, Available online ,
doi: 10.13203/j.whugis20200664
Abstract:
With the development of deep-space technology of China, the Jupiter exploration program has been added into the schedule. The precise orbit determination (POD) and gravity field recovery play an important role in Jupiter exploration. The paper focuses on the precise orbit determination of Juno and low degree gravity field recovery of Jupiter. First at all, the coordinate system and dynamics model of the Jupiter probe are given, and the Juno precision ephemeris of JPL is used for verification. The dynamical fitting position difference is on the order of 10 m, and the velocity difference is less than 6 mm/s. Then, the deep-space Doppler measurement model is presented and the trajectory of Juno is calculated using the tracking data. The difference with the reference orbit given by JPL is better than 1 kilometer. Finally, the simulation data is used to verify the reliability of gravity field solution, and the measured data near 4 perijove points of the Juno is used to calculate the gravity field coefficients of Jupiter, obtaining the gravity field zonal coefficients up to 8 degrees.
With the development of deep-space technology of China, the Jupiter exploration program has been added into the schedule. The precise orbit determination (POD) and gravity field recovery play an important role in Jupiter exploration. The paper focuses on the precise orbit determination of Juno and low degree gravity field recovery of Jupiter. First at all, the coordinate system and dynamics model of the Jupiter probe are given, and the Juno precision ephemeris of JPL is used for verification. The dynamical fitting position difference is on the order of 10 m, and the velocity difference is less than 6 mm/s. Then, the deep-space Doppler measurement model is presented and the trajectory of Juno is calculated using the tracking data. The difference with the reference orbit given by JPL is better than 1 kilometer. Finally, the simulation data is used to verify the reliability of gravity field solution, and the measured data near 4 perijove points of the Juno is used to calculate the gravity field coefficients of Jupiter, obtaining the gravity field zonal coefficients up to 8 degrees.
, Available online ,
doi: 10.13203/j.whugis20200573
Abstract:
Objectives: Geometric misalignment is one of the key problems when orthophotos stitched. To solve this problem, this paper proposes a regional seam line detection algorithm based on dense optical flow. Method: Firstly, construct the cost image with dense optical flow, gradient information and gray information, and regards the cost image as a weighted undirected graph. Secondly, using the principle of maximum flow and minimum cut based on graph-cut model self-diagnosis and search for mosaic lines. Result: The experiments show that the mosaic line cost of the sparse building aera obtained by this method only account for 0.7% of the path length, and the search efficiency is increased by 17%. Conclusion: This method can automatically avoid passing the building aera and the aera with large projection in Digital Orthophoto Map, greatly reduces the probability of geometric dislocation phenomenon of image mosaic, and realizes the intelligent of ortho image mosaic line search.
Objectives: Geometric misalignment is one of the key problems when orthophotos stitched. To solve this problem, this paper proposes a regional seam line detection algorithm based on dense optical flow. Method: Firstly, construct the cost image with dense optical flow, gradient information and gray information, and regards the cost image as a weighted undirected graph. Secondly, using the principle of maximum flow and minimum cut based on graph-cut model self-diagnosis and search for mosaic lines. Result: The experiments show that the mosaic line cost of the sparse building aera obtained by this method only account for 0.7% of the path length, and the search efficiency is increased by 17%. Conclusion: This method can automatically avoid passing the building aera and the aera with large projection in Digital Orthophoto Map, greatly reduces the probability of geometric dislocation phenomenon of image mosaic, and realizes the intelligent of ortho image mosaic line search.
, Available online ,
doi: 10.13203/j.whugis20190148
Abstract:
The echo simulation of traditional synthetic aperture sonar (SAS) often considers the signal within 3 dB beam width. Besides, it neglects the fact that the echo signal is modulated by the wideband beam pattern of the transmitter and receiver. To solve this issue, this paper investigates the echo simulation based on the modulation of the transmitter/receiver beam pattern. On the basis of that, the influence of the transmitter/receiver beam pattern on the SAS imagery is discussed using the back projection (BP) algorithm. Comparing with the processing result of the weighted array signal, the influence of the beam pattern on the imaging result is further verified. The result in this paper is useful for the suppression of the spectrum alias in azimuth. Moreover, it is also useful for the demonstration and analysis of the performance indicators related to the designed SAS system.
The echo simulation of traditional synthetic aperture sonar (SAS) often considers the signal within 3 dB beam width. Besides, it neglects the fact that the echo signal is modulated by the wideband beam pattern of the transmitter and receiver. To solve this issue, this paper investigates the echo simulation based on the modulation of the transmitter/receiver beam pattern. On the basis of that, the influence of the transmitter/receiver beam pattern on the SAS imagery is discussed using the back projection (BP) algorithm. Comparing with the processing result of the weighted array signal, the influence of the beam pattern on the imaging result is further verified. The result in this paper is useful for the suppression of the spectrum alias in azimuth. Moreover, it is also useful for the demonstration and analysis of the performance indicators related to the designed SAS system.
, Available online ,
doi: 10.13203/j.whugis20210192
Abstract:
Traditional remote sensing-based air temperature (Ta) estimation method usually used the global models, which ignored the effects of spatiotemporal heterogeneity, especially for the researches in large regional areas. Taking the Yangtze River Economic Zone as a typical research area, this paper introduced the geographically and temporally neural networks for high-precision Ta estimation. The influence of spatiotemporal heterogeneity was considered by establishing the local models in the generalized regression neural network. Remote sensing data, assimilation data and station data were fused in this study to obtain the spatially continuous near-surface Ta. The model performance was evaluated by the site-based ten-fold cross-validation method. The results showed that the geographically and temporally weighted neural network had effectively improved the estimation accuracy with the RMSE=1.899℃, MAE=1.310℃ and R=0.976. Compared with the multiple linear regression method and the traditional global neural network, the MAE value decreased by 1.112℃ and 0.378℃ respectively. The Ta mapping results indicated that the model used in this paper can well reflect the spatial distribution differences, which means that this study is possible to provide a new way for Ta estimation with high precision.
Traditional remote sensing-based air temperature (Ta) estimation method usually used the global models, which ignored the effects of spatiotemporal heterogeneity, especially for the researches in large regional areas. Taking the Yangtze River Economic Zone as a typical research area, this paper introduced the geographically and temporally neural networks for high-precision Ta estimation. The influence of spatiotemporal heterogeneity was considered by establishing the local models in the generalized regression neural network. Remote sensing data, assimilation data and station data were fused in this study to obtain the spatially continuous near-surface Ta. The model performance was evaluated by the site-based ten-fold cross-validation method. The results showed that the geographically and temporally weighted neural network had effectively improved the estimation accuracy with the RMSE=1.899℃, MAE=1.310℃ and R=0.976. Compared with the multiple linear regression method and the traditional global neural network, the MAE value decreased by 1.112℃ and 0.378℃ respectively. The Ta mapping results indicated that the model used in this paper can well reflect the spatial distribution differences, which means that this study is possible to provide a new way for Ta estimation with high precision.
, Available online ,
doi: 10.13203/j.whugis20200518
Abstract:
Objectives: Differential Interferometric Synthetic Aperture Radar (D-InSAR) has been widely used in large-scale surface deformation monitoring. However, the surface deformation obtained from spaceborne SAR data is easily affected by atmospheric noise, and the long revisit period leads to the incoherence. In order to effectively reduce these effects, this paper proposes a method to monitor the deformation of highway slope by car-borneInSAR system. Methods: Due to the orbit control of thecar-borne dual-antenna InSAR system, the spatial baseline of the interferometric pairs is close to zero. Therefore, when the D-InSAR data withthis system is used to extract the deformation information, it can avoid the flat phase, which greatly simplifies the differential interference processing process. Results: A certain area of Wuhan, Hubei Province is chosenasthe experimental area. Seven corner reflectors are deployed in the test area to evaluate the accuracy of deformation information extracted using the car-borne dual-antenna InSAR system.The deformation of seven reflectors is calculated by using the method proposed in this paper. The root mean square error of deformation between the real value and thecalculated value is 2.206 mm. Conclusions: In this paper, a deformation monitoring method of zero space baseline usingthecar-borne dual-antenna InSAR system is proposed and verified in Wuhan city. The D-InSAR method based on zero space baseline can avoid the process of the flat phase. At the same time, our system is small in size, and the design of the trajectory according to the actual needsis flexibly, which is very practical for small-scale highway slope deformation monitoring. Because of the small revisit period, the car-borne InSAR Data will not produce phase error due to atmospheric delay, and the deformation measurement accuracy is high.
Objectives: Differential Interferometric Synthetic Aperture Radar (D-InSAR) has been widely used in large-scale surface deformation monitoring. However, the surface deformation obtained from spaceborne SAR data is easily affected by atmospheric noise, and the long revisit period leads to the incoherence. In order to effectively reduce these effects, this paper proposes a method to monitor the deformation of highway slope by car-borneInSAR system. Methods: Due to the orbit control of thecar-borne dual-antenna InSAR system, the spatial baseline of the interferometric pairs is close to zero. Therefore, when the D-InSAR data withthis system is used to extract the deformation information, it can avoid the flat phase, which greatly simplifies the differential interference processing process. Results: A certain area of Wuhan, Hubei Province is chosenasthe experimental area. Seven corner reflectors are deployed in the test area to evaluate the accuracy of deformation information extracted using the car-borne dual-antenna InSAR system.The deformation of seven reflectors is calculated by using the method proposed in this paper. The root mean square error of deformation between the real value and thecalculated value is 2.206 mm. Conclusions: In this paper, a deformation monitoring method of zero space baseline usingthecar-borne dual-antenna InSAR system is proposed and verified in Wuhan city. The D-InSAR method based on zero space baseline can avoid the process of the flat phase. At the same time, our system is small in size, and the design of the trajectory according to the actual needsis flexibly, which is very practical for small-scale highway slope deformation monitoring. Because of the small revisit period, the car-borne InSAR Data will not produce phase error due to atmospheric delay, and the deformation measurement accuracy is high.
, Available online ,
doi: 10.13203/j.whugis20200567
Abstract:
Objectives Point cloud has no topological structure, current deep learning semantic segmentation algorithm is difficult to capture geometric features implied in irregular points. In addition, the point cloud is in three-dimensional space with a large amount of data size. If we blindly expand the captive field size during extract neighborhood information, it will increase the number of model parameters, which will make model training difficult. Methods To this end, we propose a point cloud semantic segmentation model based on the dilated convolution and combining elementary geometric features such as angle as the model input. First of all, during feature extraction, basic geometric features such as the relative coordinates, distance and angle between the centroid and the neighboring points are used as the model input to mine the geometric information. Secondly, in the process of building local neighborhoods, we expand the image dilated convolution operator to point cloud processing, the point cloud dilated operator can expand the receptive field size with no increasing the number of parameters of the model. Then the dilated convolution operator, multi-geometric features encoding modules and U-Net architecture are combined to form a complete point cloud semantic segmentation model. Results Semantic3D is applied to verify the proposed algorithms. The results show that compared with the traditional neighborhood structure, the OA of dilated neighborhood structure is increased by 1.4%. Compared with the model that only uses coordinates as input, multi-geometric features encoding module is increased by 10.7%. The final model based on the two proposed algorithms get mIoU and OA are 91.2% and 68.2%, respectively. Conclusions The dilated neighborhood structure can effectively extract point cloud information in a larger range without increasing the number of model parameters. multi-geometric features encoding module can maximize the capture of shape information in the neighborhood.
Objectives Point cloud has no topological structure, current deep learning semantic segmentation algorithm is difficult to capture geometric features implied in irregular points. In addition, the point cloud is in three-dimensional space with a large amount of data size. If we blindly expand the captive field size during extract neighborhood information, it will increase the number of model parameters, which will make model training difficult. Methods To this end, we propose a point cloud semantic segmentation model based on the dilated convolution and combining elementary geometric features such as angle as the model input. First of all, during feature extraction, basic geometric features such as the relative coordinates, distance and angle between the centroid and the neighboring points are used as the model input to mine the geometric information. Secondly, in the process of building local neighborhoods, we expand the image dilated convolution operator to point cloud processing, the point cloud dilated operator can expand the receptive field size with no increasing the number of parameters of the model. Then the dilated convolution operator, multi-geometric features encoding modules and U-Net architecture are combined to form a complete point cloud semantic segmentation model. Results Semantic3D is applied to verify the proposed algorithms. The results show that compared with the traditional neighborhood structure, the OA of dilated neighborhood structure is increased by 1.4%. Compared with the model that only uses coordinates as input, multi-geometric features encoding module is increased by 10.7%. The final model based on the two proposed algorithms get mIoU and OA are 91.2% and 68.2%, respectively. Conclusions The dilated neighborhood structure can effectively extract point cloud information in a larger range without increasing the number of model parameters. multi-geometric features encoding module can maximize the capture of shape information in the neighborhood.
, Available online ,
doi: 10.13203/j.whugis20200232
Abstract:
To find a high accuracy method for SCB prediction based on characteristics of SCB data, a preprocessing strategy (WMAD) of a wavelet threshold method based on the median absolute deviation(MAD) is proposed to preprocess the SCB, aiming at the small error of the middleweight of the SCB data. Firstly, the wavelet threshold method is used to decompose the SCB data to obtain the decomposed high frequency coefficient and low frequency coefficient. Then the MAD method is used to deal with the high frequency coefficient of each layer affecting the threshold setting, and the processed high frequency coefficient is used to calculate the threshold, so as to improve the ability of eliminating small outliers by the wavelet threshold method. Finally, the clock bias data of beidou-2 satellite are used to verify. The results show that this method can effectively eliminate the small error of median magnitude in the historical observation sequence of clock difference and predicting the satellite clock bias.
To find a high accuracy method for SCB prediction based on characteristics of SCB data, a preprocessing strategy (WMAD) of a wavelet threshold method based on the median absolute deviation(MAD) is proposed to preprocess the SCB, aiming at the small error of the middleweight of the SCB data. Firstly, the wavelet threshold method is used to decompose the SCB data to obtain the decomposed high frequency coefficient and low frequency coefficient. Then the MAD method is used to deal with the high frequency coefficient of each layer affecting the threshold setting, and the processed high frequency coefficient is used to calculate the threshold, so as to improve the ability of eliminating small outliers by the wavelet threshold method. Finally, the clock bias data of beidou-2 satellite are used to verify. The results show that this method can effectively eliminate the small error of median magnitude in the historical observation sequence of clock difference and predicting the satellite clock bias.
, Available online ,
doi: 10.13203/j.whugis20190286
Abstract:
In order to analsis the applicability and accuracy of TEC modelling for regional ionosphere from observations at a single station, the TEC models of 16 single-station regions in Europe are established and the TEC grids of the region are generated based on the spherical harmonic function of order from 2 to 15. The products are compared with the authoritative ionospheric products such as CODE, iGMAS and IGS. The results show that the single station regional ionospheric TEC model based on low order spherical harmonic function (2×2 or 3×3) has the same accuracy as that of global ionospheric TEC such as CODE, iGMAS and IGS, which is about 1.0TECU in the longitude and latitude range of 10×10 (radius < 600km) centered on a single station. Thus, the global ionospheric TEC model can be replaced by the ionospheric TEC model established by a single station in a certain region (radius less than 600 km), and the high-precision ionospheric delay correction can be efficiently provided for the single frequency users in the region.
In order to analsis the applicability and accuracy of TEC modelling for regional ionosphere from observations at a single station, the TEC models of 16 single-station regions in Europe are established and the TEC grids of the region are generated based on the spherical harmonic function of order from 2 to 15. The products are compared with the authoritative ionospheric products such as CODE, iGMAS and IGS. The results show that the single station regional ionospheric TEC model based on low order spherical harmonic function (2×2 or 3×3) has the same accuracy as that of global ionospheric TEC such as CODE, iGMAS and IGS, which is about 1.0TECU in the longitude and latitude range of 10×10 (radius < 600km) centered on a single station. Thus, the global ionospheric TEC model can be replaced by the ionospheric TEC model established by a single station in a certain region (radius less than 600 km), and the high-precision ionospheric delay correction can be efficiently provided for the single frequency users in the region.
, Available online ,
doi: 10.13203/j.whugis20200577
Abstract:
On July 31, 2020, China's third generation Beidou satellite navigation system (BDS-3), which operates independently, will be fully completed and officially opened for service. The signal system of BDS-3 has been redesigned to provide public service signals of five frequencies:B1I, B3I, B1C, B2a and B2b. Based on the observations of international global navigation system monitoring and assessment service (iGMAS) and Multi-GNSS experiment (MGEX) tracking stations, this paper analyzes the characteristics of pseudoranges multipath error, signal to noise ratio, Geometry-Free Ionosphere-Free (GFIF) combined observations of the new signal system, as well as the precise orbit determination (POD) performance evaluation of BDS-3 satellites. The results show that the multipath noise level of BDS-3 satellites signal is better than that of BDS-2 satellites, and there is no systematic deviation related to altitude angle, B1C is more significantly affected by multipath and noise; the GFIF sequences of different signal combinations of BDS-3 satellites show periodic systematic error related to satellite, and the peak value is about 2cm. For BDS-3 satellites, the "one-step" POD method is used. The two frequencies ionospheric free combination of B1I & B3I and B1C & B2a is adopted respectively. The orbit boundary discontinuity and satellite laser ranging (SLR) carried out an orbit accuracy check, and the results show that when the number of available observations is less than B1I & B3I, the orbit accuracy of B1C & B2a is equivalent to that of B1I & B3I, and the radial internal coincidence accuracy is 6.1cm and 6.6cm respectively.
On July 31, 2020, China's third generation Beidou satellite navigation system (BDS-3), which operates independently, will be fully completed and officially opened for service. The signal system of BDS-3 has been redesigned to provide public service signals of five frequencies:B1I, B3I, B1C, B2a and B2b. Based on the observations of international global navigation system monitoring and assessment service (iGMAS) and Multi-GNSS experiment (MGEX) tracking stations, this paper analyzes the characteristics of pseudoranges multipath error, signal to noise ratio, Geometry-Free Ionosphere-Free (GFIF) combined observations of the new signal system, as well as the precise orbit determination (POD) performance evaluation of BDS-3 satellites. The results show that the multipath noise level of BDS-3 satellites signal is better than that of BDS-2 satellites, and there is no systematic deviation related to altitude angle, B1C is more significantly affected by multipath and noise; the GFIF sequences of different signal combinations of BDS-3 satellites show periodic systematic error related to satellite, and the peak value is about 2cm. For BDS-3 satellites, the "one-step" POD method is used. The two frequencies ionospheric free combination of B1I & B3I and B1C & B2a is adopted respectively. The orbit boundary discontinuity and satellite laser ranging (SLR) carried out an orbit accuracy check, and the results show that when the number of available observations is less than B1I & B3I, the orbit accuracy of B1C & B2a is equivalent to that of B1I & B3I, and the radial internal coincidence accuracy is 6.1cm and 6.6cm respectively.
, Available online ,
doi: 10.13203/j.whugis20200721
Abstract:
Objectives: Most of the characteristics of land subsidence are analyzed separately from the perspective of temporal or spatial, and the hidden information and possible laws in the data cannot be discovered simultaneously. Temporal Principal Component Analysis (TPCA) can be used to extract temporal and spatial characteristics of temporal-spatial data in the field of geosciences. The land subsidence in the Beijing Plain has typical temporal and spatial characteristics. Therefore, TPCA makes full use of the advantages of long-term coverage of land subsidence obtained by InSAR measurement. Methods: 1. Permanent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique provide a convenient method to measure land subsidence in sub-centimeter precision. 51 Envisat ASAR data acquired from 2003 to 2010 in the Beijing Plain were used to produce 50 interferograms and obtain time-series deformation with nonlinear model. The critical steps include preprocessing like master and auxiliary images registration and registration of control points; differential interference; extraction of PS(Permanent Scatterers); removal of atmospheric phase; unwrapping to obtain the final deformation. 2. Based on the land subsidence of about 100,000 points and 51-time series, construct the original data matrix X100000*51, calculate the correlation coefficient matrix, and use the TPCA method to analyze the temporal and spatial evolution characteristics of land subsidence in the Beijing Plain. The Eigenvector from TPCA is a time series, which represents the correlation between the PC spatial pattern and the subsidence. The principal component score is the spatial pattern obtained by TPCA decomposition, which represents different spatial characteristics, and further analyzes the characteristics of the new variable-principal component score. Results: It is found that:(1) The Eigenvalues determine the amount of information explained by each component. The information explained by the first three principal components (variance contribution rate) is 86.18%, 8.66%, and 2.37% respectively. (2) The eigenvector represents the degree of correlation between the principal component scores after linear combination and the original variables, and also represents the time trend of the principal component features. The eigenvector of PC1 remained stable around 0.15, indicating that the development trend of land subsidence remained consistent during this period. The overall variation range of the feature vectors of PC2 and PC3 is larger than that of PC1. The PC2 and PC3 can reveal some seasonal variation characteristics of land subsidence through further calculation. (3) The first principal component obtained by TPCA analysis represents the long-term development trend of the spatial distribution of land subsidence. (4) The area that the second principal component that is positive has a correlation in spatial distribution with the area of compressible layer thickness above 130m. (5) The PS points where the first principal component scores are negative and the second principal component scores are positive are distributed in the severe subsidence area above 30mm/a. There is an obvious classification of land subsidence and seasonal variation between north and south area in the severe subsidence area. Specifically, in the northern subsidence area, the amount of subsidence in spring and summer is larger than in autumn and winter, it is an opposite variation in the southern subsidence area. Conclusions: In general, the temporal and spatial variation of land subsidence could be studied for urban safety monitoring by TPCA. It also can identify the main characteristics of the space and the law of temporal and spatial evolution. Since TPCA is a linear combination, by finding the direction with the largest variance for projection, the variables obtained are just uncorrelated and not independent of each other. PCA only uses the second-order statistical information of the original data and ignores its high-order statistical information. Therefore, it is necessary to optimize by rotating principal components to find more physical meanings of principal components.
Objectives: Most of the characteristics of land subsidence are analyzed separately from the perspective of temporal or spatial, and the hidden information and possible laws in the data cannot be discovered simultaneously. Temporal Principal Component Analysis (TPCA) can be used to extract temporal and spatial characteristics of temporal-spatial data in the field of geosciences. The land subsidence in the Beijing Plain has typical temporal and spatial characteristics. Therefore, TPCA makes full use of the advantages of long-term coverage of land subsidence obtained by InSAR measurement. Methods: 1. Permanent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique provide a convenient method to measure land subsidence in sub-centimeter precision. 51 Envisat ASAR data acquired from 2003 to 2010 in the Beijing Plain were used to produce 50 interferograms and obtain time-series deformation with nonlinear model. The critical steps include preprocessing like master and auxiliary images registration and registration of control points; differential interference; extraction of PS(Permanent Scatterers); removal of atmospheric phase; unwrapping to obtain the final deformation. 2. Based on the land subsidence of about 100,000 points and 51-time series, construct the original data matrix X100000*51, calculate the correlation coefficient matrix, and use the TPCA method to analyze the temporal and spatial evolution characteristics of land subsidence in the Beijing Plain. The Eigenvector from TPCA is a time series, which represents the correlation between the PC spatial pattern and the subsidence. The principal component score is the spatial pattern obtained by TPCA decomposition, which represents different spatial characteristics, and further analyzes the characteristics of the new variable-principal component score. Results: It is found that:(1) The Eigenvalues determine the amount of information explained by each component. The information explained by the first three principal components (variance contribution rate) is 86.18%, 8.66%, and 2.37% respectively. (2) The eigenvector represents the degree of correlation between the principal component scores after linear combination and the original variables, and also represents the time trend of the principal component features. The eigenvector of PC1 remained stable around 0.15, indicating that the development trend of land subsidence remained consistent during this period. The overall variation range of the feature vectors of PC2 and PC3 is larger than that of PC1. The PC2 and PC3 can reveal some seasonal variation characteristics of land subsidence through further calculation. (3) The first principal component obtained by TPCA analysis represents the long-term development trend of the spatial distribution of land subsidence. (4) The area that the second principal component that is positive has a correlation in spatial distribution with the area of compressible layer thickness above 130m. (5) The PS points where the first principal component scores are negative and the second principal component scores are positive are distributed in the severe subsidence area above 30mm/a. There is an obvious classification of land subsidence and seasonal variation between north and south area in the severe subsidence area. Specifically, in the northern subsidence area, the amount of subsidence in spring and summer is larger than in autumn and winter, it is an opposite variation in the southern subsidence area. Conclusions: In general, the temporal and spatial variation of land subsidence could be studied for urban safety monitoring by TPCA. It also can identify the main characteristics of the space and the law of temporal and spatial evolution. Since TPCA is a linear combination, by finding the direction with the largest variance for projection, the variables obtained are just uncorrelated and not independent of each other. PCA only uses the second-order statistical information of the original data and ignores its high-order statistical information. Therefore, it is necessary to optimize by rotating principal components to find more physical meanings of principal components.
, Available online ,
doi: 10.13203/j.whugis20210353
Abstract:
In view of the problems of high miss-alarm rate of small targets, heavy model weight, and detection speed that fails to meet real-time requirements in side-scan sonar shipwreck detection method based on the YOLOv3 model. The paper introduces the YOLOv5 algorithm and proposes a model based on YOLOv5 according to the characteristics of the side-scan sonar shipwreck dateset.Try YOLOv5a, YOLOv5b, YOLOv5c, YOLOv5d, YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x under the basic framework of YOLOv5 with eight different depth and width model structures.Choose the best structure use GA+K (Genetic Algorithm+K-mean) algorithm to optimize the detection frame, and improve the loss function through CIOU_Loss. The experimental results show that the improved YOLOv5a model is 0.3% and 0.6% higher than the original model in AP_0.5 and AP_0.5-0.9, and has a substantial improvement compared with the YOLOv3 model, in which AP_0.5 and AP_0.5-0.9 are improved by 4.2% and 6.1% and the the detection speed reaches 426 frames per second, which is almost doubled that of YOLOv3, which is more conducive to practical applications and engineering deployment.
In view of the problems of high miss-alarm rate of small targets, heavy model weight, and detection speed that fails to meet real-time requirements in side-scan sonar shipwreck detection method based on the YOLOv3 model. The paper introduces the YOLOv5 algorithm and proposes a model based on YOLOv5 according to the characteristics of the side-scan sonar shipwreck dateset.Try YOLOv5a, YOLOv5b, YOLOv5c, YOLOv5d, YOLOv5s, YOLOv5m, YOLOv5l and YOLOv5x under the basic framework of YOLOv5 with eight different depth and width model structures.Choose the best structure use GA+K (Genetic Algorithm+K-mean) algorithm to optimize the detection frame, and improve the loss function through CIOU_Loss. The experimental results show that the improved YOLOv5a model is 0.3% and 0.6% higher than the original model in AP_0.5 and AP_0.5-0.9, and has a substantial improvement compared with the YOLOv3 model, in which AP_0.5 and AP_0.5-0.9 are improved by 4.2% and 6.1% and the the detection speed reaches 426 frames per second, which is almost doubled that of YOLOv3, which is more conducive to practical applications and engineering deployment.
, Available online ,
doi: 10.13203/j.whugis20200143
Abstract:
Objective: The deep learning technology has made a breakthrough in many fields, and the automated map generalization based on the deep neural network is highly expected. Building generalization is a typical generalization case and also can be abstracted and interpreted as the process of encoding and decoding. It is worth systematically studying the simulation of building generalization with encoder-decoder based deep neural networks. Method: Samples are drawn from vector buildings via the segmentation of spatial data and the conversion between vector data and raster data, which is based on the based on a natural principle of objective generalization (i.e., the legibility principle drawn by scale). The encoder-decoder based deep neural networks are trained and tested with these samples to learn buildings generalization and to evaluate this application. Five representative models based on encoder-decoder structure are constructed for learning of generalizing buildings from 1:10000 to 1:50000. The experimental data cover 44.1km×38.6km and contain 89539 and 21732 buildings before and after generalization respectively. Five representative models are EDnet, Unet, ResUnet, Unet++ and Pix2Pix representing the basic multi-layers encoder-decoder neural network, the encoder-decoder neural network with skip connection, the residual encoder-decoder neural network, the dense encoder-decoder neural network, and the encoder-decoder neural network with generative adversarial mechanism respectively. The training process and tested results of the three constructed models are compared, and effects of these five models while they are applied to the automated generalization of buildings are analyzed. Results: The pixel accuracies of predicted results during EDnet training and testing are lowest obviously, Unet takes the second lowest place, and the pixel accuracies of results generated by other models (ResUnet, Unet++ and Pix2Pix) are higher and relatively close. Additionally, there are some holes and fuzzy representations in generalized buildings predicted by ResUnet and Unet++, which does not conform with the regular representation of buildings. Generalization results predicted by the Pix2Pix show that most of generalized building edges are clear and generalized buildings can be represented independently and completely. Conclusions: The Pix2Pix is more suitable than the other four models for learning and simulating buildings generalization in our experiment. The encoder-decoder based deep neural networks are likely to deduce some knowledge and operators of buildings generalization by learning from samples of the original and generalized data. The encoder-decoder based deep neural networks have the potential in the learning of map generalization.
Objective: The deep learning technology has made a breakthrough in many fields, and the automated map generalization based on the deep neural network is highly expected. Building generalization is a typical generalization case and also can be abstracted and interpreted as the process of encoding and decoding. It is worth systematically studying the simulation of building generalization with encoder-decoder based deep neural networks. Method: Samples are drawn from vector buildings via the segmentation of spatial data and the conversion between vector data and raster data, which is based on the based on a natural principle of objective generalization (i.e., the legibility principle drawn by scale). The encoder-decoder based deep neural networks are trained and tested with these samples to learn buildings generalization and to evaluate this application. Five representative models based on encoder-decoder structure are constructed for learning of generalizing buildings from 1:10000 to 1:50000. The experimental data cover 44.1km×38.6km and contain 89539 and 21732 buildings before and after generalization respectively. Five representative models are EDnet, Unet, ResUnet, Unet++ and Pix2Pix representing the basic multi-layers encoder-decoder neural network, the encoder-decoder neural network with skip connection, the residual encoder-decoder neural network, the dense encoder-decoder neural network, and the encoder-decoder neural network with generative adversarial mechanism respectively. The training process and tested results of the three constructed models are compared, and effects of these five models while they are applied to the automated generalization of buildings are analyzed. Results: The pixel accuracies of predicted results during EDnet training and testing are lowest obviously, Unet takes the second lowest place, and the pixel accuracies of results generated by other models (ResUnet, Unet++ and Pix2Pix) are higher and relatively close. Additionally, there are some holes and fuzzy representations in generalized buildings predicted by ResUnet and Unet++, which does not conform with the regular representation of buildings. Generalization results predicted by the Pix2Pix show that most of generalized building edges are clear and generalized buildings can be represented independently and completely. Conclusions: The Pix2Pix is more suitable than the other four models for learning and simulating buildings generalization in our experiment. The encoder-decoder based deep neural networks are likely to deduce some knowledge and operators of buildings generalization by learning from samples of the original and generalized data. The encoder-decoder based deep neural networks have the potential in the learning of map generalization.
, Available online ,
doi: 10.13203/j.whugis20200631
Abstract:
Objectives: Researchers notice that the quality of training samples will impact the effective of training phase and then further will have an influence on the overall classification accuracy in the testing phase. In fact, representativeness or typicalness of training samples is able to reflect the quality of training samples in a way. Especially for the currently popular deep learning methods, it has needed thousands or millions of training samples. Therefore, how to reduce the number of training samples for deep learning method becomes a very important problem. In another hand, from the actual application angle, it is also very expensive. Therefore, we research one method of reducing the training samples as less as possible based on the representativeness or typicalness of training samples. Method: selection of training samples based on oblique factor model is proposed and it relaxes the independent condition among common factors in the orthogonal factor model, which is able to better describe the real world. Results: Experimental results show the proposed method is feasible and effective and it is able to select more representative training samples than the method of selection of training samples based on orthogonal factor model and achieve better performance in the overall classification precision and stability. Experimental results show that selection of training samples based on oblique factor model outperforms selection of training samples based on orthogonal factor model. And the distribution of selected samples becomes more decentralized and reasonable and the overall classification accuracy averagely improves about 3%. Conclusions: the proposed method, not only supports how to optimize capturing data in the theory, but also is able to guide how to effectively capture data in the actual application.
Objectives: Researchers notice that the quality of training samples will impact the effective of training phase and then further will have an influence on the overall classification accuracy in the testing phase. In fact, representativeness or typicalness of training samples is able to reflect the quality of training samples in a way. Especially for the currently popular deep learning methods, it has needed thousands or millions of training samples. Therefore, how to reduce the number of training samples for deep learning method becomes a very important problem. In another hand, from the actual application angle, it is also very expensive. Therefore, we research one method of reducing the training samples as less as possible based on the representativeness or typicalness of training samples. Method: selection of training samples based on oblique factor model is proposed and it relaxes the independent condition among common factors in the orthogonal factor model, which is able to better describe the real world. Results: Experimental results show the proposed method is feasible and effective and it is able to select more representative training samples than the method of selection of training samples based on orthogonal factor model and achieve better performance in the overall classification precision and stability. Experimental results show that selection of training samples based on oblique factor model outperforms selection of training samples based on orthogonal factor model. And the distribution of selected samples becomes more decentralized and reasonable and the overall classification accuracy averagely improves about 3%. Conclusions: the proposed method, not only supports how to optimize capturing data in the theory, but also is able to guide how to effectively capture data in the actual application.
, Available online ,
doi: 10.13203/j.whugis20200600
Abstract:
Objectives: As an important part of cultural heritage, immovable cultural relics are facing increasing risks of rainstorm and flood disaster. In order to improve the capabilities of prevention on rainstorm and flood disaster for immovable cultural relics, this study proposed a risk assessment method based on the theory of natural disaster risk assessment considering the seasonal variation of rainstorm and flood disaster, and took 24 National Ancient Sites in 18 counties in Fujian province as an example. Methods: The regional L-moment method was used to acquire the rainfall values of various return periods in different season, and analyze the characteristics of seasonal rainstorm and flood risk. We estimated the risk from three aspects:hazard factors, disaster formative environment and hazard bearing body of cultural heritages. The coefficient of variation method, entropy weight method and Delphi method were adopted to calculate the weight of those three aspects. Results: The results show that the seasonal difference of the risk assessment results is obvious, which indicates that the assessment method is feasible. The risk of rainstorm and flood was the highest in the second quarter, followed by the first and third quarter, and the spatial distribution of risk in different seasons was significantly different. In the first and second quarters, the risk of rainstorm and flood was high in coastal and northern counties, and low in central counties. The risks in the third and fourth quarters were high in the coastal areas and low in the inland areas. Conclusions: The proposed model for immovable cultural relics can well reflect the seasonal difference of rainstorm and flood risk, and is suitable for regions with large seasonal difference of rainstorm and flood disaster. The results of seasonal differences can provide scientific reference for disaster prevention and mitigation planning for immovable cultural relics.
Objectives: As an important part of cultural heritage, immovable cultural relics are facing increasing risks of rainstorm and flood disaster. In order to improve the capabilities of prevention on rainstorm and flood disaster for immovable cultural relics, this study proposed a risk assessment method based on the theory of natural disaster risk assessment considering the seasonal variation of rainstorm and flood disaster, and took 24 National Ancient Sites in 18 counties in Fujian province as an example. Methods: The regional L-moment method was used to acquire the rainfall values of various return periods in different season, and analyze the characteristics of seasonal rainstorm and flood risk. We estimated the risk from three aspects:hazard factors, disaster formative environment and hazard bearing body of cultural heritages. The coefficient of variation method, entropy weight method and Delphi method were adopted to calculate the weight of those three aspects. Results: The results show that the seasonal difference of the risk assessment results is obvious, which indicates that the assessment method is feasible. The risk of rainstorm and flood was the highest in the second quarter, followed by the first and third quarter, and the spatial distribution of risk in different seasons was significantly different. In the first and second quarters, the risk of rainstorm and flood was high in coastal and northern counties, and low in central counties. The risks in the third and fourth quarters were high in the coastal areas and low in the inland areas. Conclusions: The proposed model for immovable cultural relics can well reflect the seasonal difference of rainstorm and flood risk, and is suitable for regions with large seasonal difference of rainstorm and flood disaster. The results of seasonal differences can provide scientific reference for disaster prevention and mitigation planning for immovable cultural relics.
, Available online ,
doi: 10.13203/j.whugis20210001
Abstract:
Forest canopy density is an important factor in forest resource surveys. It plays an important role in forest quality evaluation and forest resource management. In recent years, artificial intelligence technology and remote sensing technology have developed rapidly and they have been successfully applied to forestry remote sensing quantitative estimation. There is a great significance to study how to use deep learning methods to effectively integrate the remote sensing data with different spatial coverage capabilities in regional forest canopy closure estimation. Therefore, this research proposed a deep learning model (UnetR) which aimed to estimate forest canopy closure based on deep learning model with the high-density light detection and ranging and high spatial resolution satellite imagery. By optimizing the loss function of Unet for image classification, and adding a batch normalization layer after the convolution layer, the model had the ability to quantitatively estimate continuous variables. The comparative evaluation results with fully convolutional networks (FCN), random forest (RF) and support vector regression (SVR) models showed that the root mean square error of the UnetR model was lower, the estimation accuracy was higher (determination coefficient (R2)=0.777, root mean square error (RMSE)=0.137, estimation accuracy (EA)=75.60%). This research provided a low labor cost and high degree of automation estimation method for remote sensing monitoring of regional forest canopy closure.
Forest canopy density is an important factor in forest resource surveys. It plays an important role in forest quality evaluation and forest resource management. In recent years, artificial intelligence technology and remote sensing technology have developed rapidly and they have been successfully applied to forestry remote sensing quantitative estimation. There is a great significance to study how to use deep learning methods to effectively integrate the remote sensing data with different spatial coverage capabilities in regional forest canopy closure estimation. Therefore, this research proposed a deep learning model (UnetR) which aimed to estimate forest canopy closure based on deep learning model with the high-density light detection and ranging and high spatial resolution satellite imagery. By optimizing the loss function of Unet for image classification, and adding a batch normalization layer after the convolution layer, the model had the ability to quantitatively estimate continuous variables. The comparative evaluation results with fully convolutional networks (FCN), random forest (RF) and support vector regression (SVR) models showed that the root mean square error of the UnetR model was lower, the estimation accuracy was higher (determination coefficient (R2)=0.777, root mean square error (RMSE)=0.137, estimation accuracy (EA)=75.60%). This research provided a low labor cost and high degree of automation estimation method for remote sensing monitoring of regional forest canopy closure.
, Available online ,
doi: 10.13203/j.whugis20200306
Abstract:
It is a very important highly accurate application of optical video satellites to discover a moving target and obtain its geographic position and moving speed. Although the existing stabilization approaches for optical video satellites can obtain stable and smooth videos, the lack of geometric information in the satellite videos makes them difficult to meet the above application requirements. To solve this problem, a geo-coded stabilization approach for optical video satellites in object space is proposed in this paper. The proposed approach takes full advantage of the orientation parameter information of satellite video frames. An interframe motion model based on the video frame orientation model is first established. Then, the orientation parameters of the auxiliary frame are motion compensated to achieve the geometric consistency between the video frames. Finally, all the video frames are geo-coded in object space, and the geo-coded smooth video can be generated. The experimental results of a ZhuHai-1 satellite video showed that the proposed approach could effectively eliminate the influence of satellite jitter errors and satellite position and attitude errors. Both the geometric accuracy between the video frames and the video stabilization accuracy achieved by the proposed approach reached better than 0.3 pixels.
It is a very important highly accurate application of optical video satellites to discover a moving target and obtain its geographic position and moving speed. Although the existing stabilization approaches for optical video satellites can obtain stable and smooth videos, the lack of geometric information in the satellite videos makes them difficult to meet the above application requirements. To solve this problem, a geo-coded stabilization approach for optical video satellites in object space is proposed in this paper. The proposed approach takes full advantage of the orientation parameter information of satellite video frames. An interframe motion model based on the video frame orientation model is first established. Then, the orientation parameters of the auxiliary frame are motion compensated to achieve the geometric consistency between the video frames. Finally, all the video frames are geo-coded in object space, and the geo-coded smooth video can be generated. The experimental results of a ZhuHai-1 satellite video showed that the proposed approach could effectively eliminate the influence of satellite jitter errors and satellite position and attitude errors. Both the geometric accuracy between the video frames and the video stabilization accuracy achieved by the proposed approach reached better than 0.3 pixels.
, Available online ,
doi: 10.13203/j.whugis20200714
Abstract:
Objectives: BDS-3 officially opened its global service on July 31, 2020. To evaluate the BDS-3 global positioning performance in detail. Based on multi-day measured data from 16 MGEX tracking stations around the world. Methods: Net_Diff software was used to carry out the global BDS-3 single-frequency, dual-frequency non-ionospheric combination and dual-frequency non-combination model under dual-frequency, three-frequency non-combination model and three-frequency non-ionospheric pairwise combination model, three-frequency pseudorange single Point positioning solution experiment. And compare with GPS, Galileo some frequencies. Results: The results show that the number of satellites and spatial geometry of BDS-3 in Asia, Europe and Africa are better than GPS and Galileo. For BDS-3 single-frequency pseudo-range single-point positioning, the four frequencies of B1C, B1I, B2a, and B3I have four frequencies of E direction accuracy. It is better than 0.5 m, the accuracy of the N direction is better than 1 m, and the accuracy of the U direction is better than 2 m. Compared with GPS and Galileo, the positioning accuracy relationship is:B1C>B1I>L1>B3I>B2a>E1>L2>E5a. For BDS-3 dual-frequency combined pseudorange single-point positioning, the B2aB3I combined positioning is poor and not suitable for positioning. The B1CB2a, B1CB3I, B1IB2a, B1IB3I combined frequency positioning accuracy in the E and N directions is better than 1m, and the positioning accuracy in the U direction is better 2 m, compared with GPS and Galileo, the positioning accuracy relationship is:B1CB2a> B1CB3I> L1L2> B1IB3I> B1IB2a> E1E5a> B2aB3I. For BDS-3 triple-frequency combined pseudo-range single-point positioning, the positioning accuracy of B1IB2aB3I and B1CB2aB3I in the E and N directions is better than 1 m, and the positioning accuracy in the U direction is better than 2 m. Compared with GPS and Galileo, the positioning accuracy relationship is:B1CB2aB3I> B1IB2aB3I> L1L2L2> E1E5aE5b. B1CB3I, B1IB3I, B1CB2a, B1IB2a dual-frequency combinations are suitable for positioning using non-combined models, B2aB3I is suitable for positioning using non-ionospheric models, and B1IB2aB3I and B1CB2aB3I are suitable for positioning using non-combined models. Conclusions: Combining the experimental results can draw conclusions, BDS-3 has better positioning performance on a global scale. Even some frequency positioning performance is better than GPS and Galileo, which can provide a certain reference for future BDS-3 related research.
Objectives: BDS-3 officially opened its global service on July 31, 2020. To evaluate the BDS-3 global positioning performance in detail. Based on multi-day measured data from 16 MGEX tracking stations around the world. Methods: Net_Diff software was used to carry out the global BDS-3 single-frequency, dual-frequency non-ionospheric combination and dual-frequency non-combination model under dual-frequency, three-frequency non-combination model and three-frequency non-ionospheric pairwise combination model, three-frequency pseudorange single Point positioning solution experiment. And compare with GPS, Galileo some frequencies. Results: The results show that the number of satellites and spatial geometry of BDS-3 in Asia, Europe and Africa are better than GPS and Galileo. For BDS-3 single-frequency pseudo-range single-point positioning, the four frequencies of B1C, B1I, B2a, and B3I have four frequencies of E direction accuracy. It is better than 0.5 m, the accuracy of the N direction is better than 1 m, and the accuracy of the U direction is better than 2 m. Compared with GPS and Galileo, the positioning accuracy relationship is:B1C>B1I>L1>B3I>B2a>E1>L2>E5a. For BDS-3 dual-frequency combined pseudorange single-point positioning, the B2aB3I combined positioning is poor and not suitable for positioning. The B1CB2a, B1CB3I, B1IB2a, B1IB3I combined frequency positioning accuracy in the E and N directions is better than 1m, and the positioning accuracy in the U direction is better 2 m, compared with GPS and Galileo, the positioning accuracy relationship is:B1CB2a> B1CB3I> L1L2> B1IB3I> B1IB2a> E1E5a> B2aB3I. For BDS-3 triple-frequency combined pseudo-range single-point positioning, the positioning accuracy of B1IB2aB3I and B1CB2aB3I in the E and N directions is better than 1 m, and the positioning accuracy in the U direction is better than 2 m. Compared with GPS and Galileo, the positioning accuracy relationship is:B1CB2aB3I> B1IB2aB3I> L1L2L2> E1E5aE5b. B1CB3I, B1IB3I, B1CB2a, B1IB2a dual-frequency combinations are suitable for positioning using non-combined models, B2aB3I is suitable for positioning using non-ionospheric models, and B1IB2aB3I and B1CB2aB3I are suitable for positioning using non-combined models. Conclusions: Combining the experimental results can draw conclusions, BDS-3 has better positioning performance on a global scale. Even some frequency positioning performance is better than GPS and Galileo, which can provide a certain reference for future BDS-3 related research.
, Available online ,
doi: 10.13203/j.whugis20200640
Abstract:
As the hardware of remote sensing sensors continuously strengthened, the high-spatial resolution remote sensing images are widely used.However, typical classification algorithms have poor accuracy and efficiency in classifying high-resolution remote sensing images, and deep learning semantic segmentation algorithm also has poor generalization in the classification of high-resolution remote sensing images, which make the semantic segmentation of high-resolution images more challenging. In order to adapt to the large-scale high-resolution images, we design a semantic segmentation model of simulated annealing hyper parameter optimization and depth wise separable convolution based on U-Net (Baseline). Firstly, the depth wise separable convolutional module is used to extract the features on the baseline. Then the intelligent optimization learning model based on simulated annealing searches the global optimal solution of the hyper parameters. Finally, experiments were carried out on the ISPRS2D and GID(Gaofen Image Dataset). Compared to other methods, the accuracies of the classification results of the ISPRS2D Dataset for buildings, low vegetations and cars, as well as the general classification were improved based on the proposed method. In the classification results of GID, the accuracies of water, grassland, forest and general classification have been greatly improved by the proposed method. The results of the studies demonstrated the higher efficiency, higher accuracy and robustness of the proposed model.
As the hardware of remote sensing sensors continuously strengthened, the high-spatial resolution remote sensing images are widely used.However, typical classification algorithms have poor accuracy and efficiency in classifying high-resolution remote sensing images, and deep learning semantic segmentation algorithm also has poor generalization in the classification of high-resolution remote sensing images, which make the semantic segmentation of high-resolution images more challenging. In order to adapt to the large-scale high-resolution images, we design a semantic segmentation model of simulated annealing hyper parameter optimization and depth wise separable convolution based on U-Net (Baseline). Firstly, the depth wise separable convolutional module is used to extract the features on the baseline. Then the intelligent optimization learning model based on simulated annealing searches the global optimal solution of the hyper parameters. Finally, experiments were carried out on the ISPRS2D and GID(Gaofen Image Dataset). Compared to other methods, the accuracies of the classification results of the ISPRS2D Dataset for buildings, low vegetations and cars, as well as the general classification were improved based on the proposed method. In the classification results of GID, the accuracies of water, grassland, forest and general classification have been greatly improved by the proposed method. The results of the studies demonstrated the higher efficiency, higher accuracy and robustness of the proposed model.
, Available online ,
doi: 10.13203/j.whugis20210147
Abstract:
Objectives: In the field of line matching and checking, the geometric attributes of the individual line are weakly stable, and non one-to-one matching results are difficult to be checked. To address the problems above, a line matching algorithm based on pair-wise geometric features and individual line descriptor constraints was proposed. Methods: For matching process, firstly two line segments which satisfied certain geometric constraints in the neighborhood were grouped into a line pair and matched as a whole. Secondly, the epipolar constraint of the intersection in the line pair was used to determine the matching range. Then characteristic angles within line pairs, distance ratio between line segments and the radiation information of the neighborhood of line pairs were used to narrow the range of the matching candidates. Finally, the final matching pairs was obtained by caculating the gray similarity of triangle region. For checking process, initially according to the angle of line with epipolar and the slope of line, the corresponding relation between individual lines was established. Moreover, each corresponding line pair was split into two groups of corresponding individual lines. Then, descriptors were established for two single lines in each group and the similarity between the two line descriptors was calculated. Eventually, collinear geometry and descriptor similarity were combined to check the matching results, eliminate the false matches. Collinear lines in the results were merged and one-to-one matching results were obtained. Results: Aerial images with typical texture features and close-range images of different transformation types were selected for experiments. The results demonstrate that the proposed algorithm has a high matching accuracy. The matching accuracy rate is higher than 95% in complex scenes with similar texture, perspective change, rotation change, scale change and illumination change. Conclusions: The proposed algorithm has good robustness to different types of images, and also has advantages for complex line matching check.
Objectives: In the field of line matching and checking, the geometric attributes of the individual line are weakly stable, and non one-to-one matching results are difficult to be checked. To address the problems above, a line matching algorithm based on pair-wise geometric features and individual line descriptor constraints was proposed. Methods: For matching process, firstly two line segments which satisfied certain geometric constraints in the neighborhood were grouped into a line pair and matched as a whole. Secondly, the epipolar constraint of the intersection in the line pair was used to determine the matching range. Then characteristic angles within line pairs, distance ratio between line segments and the radiation information of the neighborhood of line pairs were used to narrow the range of the matching candidates. Finally, the final matching pairs was obtained by caculating the gray similarity of triangle region. For checking process, initially according to the angle of line with epipolar and the slope of line, the corresponding relation between individual lines was established. Moreover, each corresponding line pair was split into two groups of corresponding individual lines. Then, descriptors were established for two single lines in each group and the similarity between the two line descriptors was calculated. Eventually, collinear geometry and descriptor similarity were combined to check the matching results, eliminate the false matches. Collinear lines in the results were merged and one-to-one matching results were obtained. Results: Aerial images with typical texture features and close-range images of different transformation types were selected for experiments. The results demonstrate that the proposed algorithm has a high matching accuracy. The matching accuracy rate is higher than 95% in complex scenes with similar texture, perspective change, rotation change, scale change and illumination change. Conclusions: The proposed algorithm has good robustness to different types of images, and also has advantages for complex line matching check.
, Available online ,
doi: 10.13203/j.whugis20200686
Abstract:
With the ability of providing Real-Time Precise Point Positioning (RTPPP) service for China and surrounding countries, the application of BeiDou Navigation Satellite System(BDS-3) PPP-B2b signals have seen a rapid rise in recent years. And the performance evaluation is a vital issue for large deployment in the future, such as the evaluation about the accuracy of orbit and clock error, as well as the analysis of precision and convergence time for Precise Point Positioning(PPP). We hereby follow this idea in this paper. Based on the September observations of the China branch stations of international GNSS Monitoring and Assessment System(iGMAS), the accuracy of orbit and clock error, as well as the positioning accuracy of B1I+B3I and B1C+B2a signal combination are carefully evaluated in this dissertation. The results show that the average accuracy of PPP-B2b orbit products in R, A and C direction is 0.1 m, 0.31 m and 0.3 m, respectively. Besides, the root mean square (RMS) of clock error correction is 2.26 ns, In terms of PPP convergence, the B1I+B3 signal combination using GBM products is the quickest among them, with the highest final convergency accuracy. The second is B1I + B3I (PPP-B2b), and B1C + B2a(PPP-B2b) is at the last. Summarizing the above discussions, BDS-3 PPP-B2b signals is capable of providing regional PPP service in China.
With the ability of providing Real-Time Precise Point Positioning (RTPPP) service for China and surrounding countries, the application of BeiDou Navigation Satellite System(BDS-3) PPP-B2b signals have seen a rapid rise in recent years. And the performance evaluation is a vital issue for large deployment in the future, such as the evaluation about the accuracy of orbit and clock error, as well as the analysis of precision and convergence time for Precise Point Positioning(PPP). We hereby follow this idea in this paper. Based on the September observations of the China branch stations of international GNSS Monitoring and Assessment System(iGMAS), the accuracy of orbit and clock error, as well as the positioning accuracy of B1I+B3I and B1C+B2a signal combination are carefully evaluated in this dissertation. The results show that the average accuracy of PPP-B2b orbit products in R, A and C direction is 0.1 m, 0.31 m and 0.3 m, respectively. Besides, the root mean square (RMS) of clock error correction is 2.26 ns, In terms of PPP convergence, the B1I+B3 signal combination using GBM products is the quickest among them, with the highest final convergency accuracy. The second is B1I + B3I (PPP-B2b), and B1C + B2a(PPP-B2b) is at the last. Summarizing the above discussions, BDS-3 PPP-B2b signals is capable of providing regional PPP service in China.
, Available online ,
doi: 10.13203/j.whugis20210143
Abstract:
Objectives: Land subsidence in urban areas brings loss of ground elevation, damages urban infrastructure and buildings, and affects surface runoff and hydrological cycle. Monitoring the status of land subsidence and revealing its formation mechanism is of great significance for sustainable urban development. Methods: Using ALOS-PALSAR images from 2007 to 2011 and Radarsat-2 images from 2015 to 2019 as data sources, based on SBAS-InSAR technology to obtain the land subsidence rate and time series, and using geographic detectors to reveal the dominant driving factors and the interaction mechanism between the driving factors of land subsidence at the scale of planning unit. Results: The results show that:①The average land subsidence rates from 2007 to 2011 and 2015 to 2019 were -3.53 mm/year and -1.48 mm/year, respectively. The hot spots for land subsidence from 2007 to 2011 are Hankou, the coast and north of Shahu Lake, the west of Nanhu Lake, and the Baishazhou area. From 2015 to 2019, it is Hankou, the north of Shahu Lake and the Baishazhou area. ②The temporal and spatial evolution of land subsidence in Wuhan is localized, staged, and related to natural conditions and human activities. Rapid subsidence only occur in certain region, shows the different trends at various stages, and is closely related to the regional natural conditions and human activities. ③Hydrogeological conditions are necessary conditions to form the spatial-temporal pattern of land subsidence in Wuhan through interaction with various factors such as ground load, underground space development, and engineering construction. The interactive effects between engineering construction and hydrogeological conditions from 2007 to 2011 are found to be significant, so do ground load and hydrogeological conditions from 2015 to 2019. Conclusion: The geographic detector can quantitatively identify the driving factors of land subsidence and the interactions between them. The interactive effects between hydrogeological and conditions engineering construction, ground load to a large extent affected spatial variation of land subsidence from 2007 to 2011 and 2015 to 2019 respectively. In the future, continuous monitoring of land subsidence and multi-scale research on the formation mechanism of land subsidence should be carried out to further enrich the theory and method system of land subsidence research.
Objectives: Land subsidence in urban areas brings loss of ground elevation, damages urban infrastructure and buildings, and affects surface runoff and hydrological cycle. Monitoring the status of land subsidence and revealing its formation mechanism is of great significance for sustainable urban development. Methods: Using ALOS-PALSAR images from 2007 to 2011 and Radarsat-2 images from 2015 to 2019 as data sources, based on SBAS-InSAR technology to obtain the land subsidence rate and time series, and using geographic detectors to reveal the dominant driving factors and the interaction mechanism between the driving factors of land subsidence at the scale of planning unit. Results: The results show that:①The average land subsidence rates from 2007 to 2011 and 2015 to 2019 were -3.53 mm/year and -1.48 mm/year, respectively. The hot spots for land subsidence from 2007 to 2011 are Hankou, the coast and north of Shahu Lake, the west of Nanhu Lake, and the Baishazhou area. From 2015 to 2019, it is Hankou, the north of Shahu Lake and the Baishazhou area. ②The temporal and spatial evolution of land subsidence in Wuhan is localized, staged, and related to natural conditions and human activities. Rapid subsidence only occur in certain region, shows the different trends at various stages, and is closely related to the regional natural conditions and human activities. ③Hydrogeological conditions are necessary conditions to form the spatial-temporal pattern of land subsidence in Wuhan through interaction with various factors such as ground load, underground space development, and engineering construction. The interactive effects between engineering construction and hydrogeological conditions from 2007 to 2011 are found to be significant, so do ground load and hydrogeological conditions from 2015 to 2019. Conclusion: The geographic detector can quantitatively identify the driving factors of land subsidence and the interactions between them. The interactive effects between hydrogeological and conditions engineering construction, ground load to a large extent affected spatial variation of land subsidence from 2007 to 2011 and 2015 to 2019 respectively. In the future, continuous monitoring of land subsidence and multi-scale research on the formation mechanism of land subsidence should be carried out to further enrich the theory and method system of land subsidence research.
, Available online ,
doi: 10.13203/j.whugis20200324
Abstract:
For the difficult problem of balanced control of the computational efficiency and boundary position accuracy of the existing Voronoi algorithms based on the idea of figure discretization, we proposed an adaptive Voronoi diagram algorithm for multi-scale areal residential area matching based on interpolation strategy according to neighbor pair type——AVARA. Firstly, it uses the Delaunay triangle network composed of the centroids of the residential areas to calculate the neighbor pairs of the residential areas. Secondly, it classifies neighbor pairs according to the size relationship between the minimum distance between the neighbor pairs and the minimum side length of their minimum area bounding rectangles. Then it adaptively interpolates points on the boundary of the residential area according to the neighbor pair type. Finally, it constructs the Voronoi diagram for residential areas based on the interpolated point set and the vertex set of the residential area. We conducted the experiments of creating Voronoi diagram by using the residential area dataset which map scale is 1:10,000 and 1:50,000. The results showed that:compared with the intervisibility point method, the 3m and 6m interval interpolation point method of 1:10,000 data, AVARA outperformed the local position accuracy and time performance, compared with the 30m interval interpolation point method of 1:50,000 data, AVARA achieved higher local position accuracy, and compared with the 15m interval interpolation point method of 1:50,000 data, AVARA time performance has been improved by 34.6%.The problem of balance control between computational efficiency and boundary position accuracy of Voronoi diagram was alleviated effectively.
For the difficult problem of balanced control of the computational efficiency and boundary position accuracy of the existing Voronoi algorithms based on the idea of figure discretization, we proposed an adaptive Voronoi diagram algorithm for multi-scale areal residential area matching based on interpolation strategy according to neighbor pair type——AVARA. Firstly, it uses the Delaunay triangle network composed of the centroids of the residential areas to calculate the neighbor pairs of the residential areas. Secondly, it classifies neighbor pairs according to the size relationship between the minimum distance between the neighbor pairs and the minimum side length of their minimum area bounding rectangles. Then it adaptively interpolates points on the boundary of the residential area according to the neighbor pair type. Finally, it constructs the Voronoi diagram for residential areas based on the interpolated point set and the vertex set of the residential area. We conducted the experiments of creating Voronoi diagram by using the residential area dataset which map scale is 1:10,000 and 1:50,000. The results showed that:compared with the intervisibility point method, the 3m and 6m interval interpolation point method of 1:10,000 data, AVARA outperformed the local position accuracy and time performance, compared with the 30m interval interpolation point method of 1:50,000 data, AVARA achieved higher local position accuracy, and compared with the 15m interval interpolation point method of 1:50,000 data, AVARA time performance has been improved by 34.6%.The problem of balance control between computational efficiency and boundary position accuracy of Voronoi diagram was alleviated effectively.
Spatiotemporal Interpolation Methods of NPP/VIIRS Sequence Images Considering Neighbor Relationships
, Available online ,
doi: 10.13203/j.whugis20200019
Abstract:
For the problem of night-time data missing in VIIRS monthly composites, this study proposes a spatiotemporal interpolation method based on the correlation of adjacent pixel features. The time-space relationship is used as a constraint condition, pixel data with better time series consistency is regard as the reference of Interpolation in spatial dimension, monthly data with good spatial relationship consistency is regard as the reference of interpolation in time dimension, by constructing different convolution kernels, the missing data is interpolated in time and space dimensions respectively, and the results of missing data are weighted by different dimensional interpolation result. Take the data of Jiangsu Province in 2015, the experimental analysis is carried out. The experimental results show that:(1) Spatial dimensional interpolation takes into account the spatial correlation of pixels, it still cannot meet the interpolation requirements of large-scale data missing, and results generally in low;(2) The time dimension interpolation takes into account the time trend of the pixel, and the interpolation precision has a certain improvement compared with the spatial dimension interpolation, but the partial interpolation result has a large deviation; (3) Compared with the three-time Hermit interpolation, the maximum relative error of the monthly image light brightness sum obtained by the spatio-temporal interpolation method, the relative error of the annual image light brightness sum, and the pixel-by-pixel difference are significantly reduced. In general, the spatiotemporal interpolation method takes into account the time stability and spatial structure stability of NPP/VIIRS data, accuracy of interpolation improved obviously, and the method does not have strict requirements on the time series data before and after interpolation month, it's more extensive.
For the problem of night-time data missing in VIIRS monthly composites, this study proposes a spatiotemporal interpolation method based on the correlation of adjacent pixel features. The time-space relationship is used as a constraint condition, pixel data with better time series consistency is regard as the reference of Interpolation in spatial dimension, monthly data with good spatial relationship consistency is regard as the reference of interpolation in time dimension, by constructing different convolution kernels, the missing data is interpolated in time and space dimensions respectively, and the results of missing data are weighted by different dimensional interpolation result. Take the data of Jiangsu Province in 2015, the experimental analysis is carried out. The experimental results show that:(1) Spatial dimensional interpolation takes into account the spatial correlation of pixels, it still cannot meet the interpolation requirements of large-scale data missing, and results generally in low;(2) The time dimension interpolation takes into account the time trend of the pixel, and the interpolation precision has a certain improvement compared with the spatial dimension interpolation, but the partial interpolation result has a large deviation; (3) Compared with the three-time Hermit interpolation, the maximum relative error of the monthly image light brightness sum obtained by the spatio-temporal interpolation method, the relative error of the annual image light brightness sum, and the pixel-by-pixel difference are significantly reduced. In general, the spatiotemporal interpolation method takes into account the time stability and spatial structure stability of NPP/VIIRS data, accuracy of interpolation improved obviously, and the method does not have strict requirements on the time series data before and after interpolation month, it's more extensive.
, Available online ,
doi: 10.13203/j.whugis20200530
Abstract:
Discrete global grid systems are the preferred data models supporting multisource geospatial information fusion. Hexagonal grids have become more popular in many applications due to their geometric characteristics within uniform adjacent. We design a uniform tiles hierarchy on the surface of the icosahedron according to the characteristics of the 4-aperture hexagonal refinement, using complex numbers to build a unified coding and operation model. We also design algorithms including interoperating between geographic coordinates and codes, querying neighborhood codes. Compared with traditional methods, the principle of the uniform tile hierarchy is simple and easy to understand, the algorithm complexity is low, and the computer execution efficiency is high. The results of the experiments indicate that interoperation between geographic coordinates and codes efficiency of the proposed algorithm is approximately 2.74 and 1.73 times that of the traditional method respectively and that neighborhood codes query efficiency of the proposed algorithm is approximately 7.46 times that of the traditional method. As the grid level rises, the advantages of the proposed method become more obvious. The research results of this paper are expected to provide theoretical and technical supports for the unified organization, management, processing and analysis of multi-source Earth observation data.
Discrete global grid systems are the preferred data models supporting multisource geospatial information fusion. Hexagonal grids have become more popular in many applications due to their geometric characteristics within uniform adjacent. We design a uniform tiles hierarchy on the surface of the icosahedron according to the characteristics of the 4-aperture hexagonal refinement, using complex numbers to build a unified coding and operation model. We also design algorithms including interoperating between geographic coordinates and codes, querying neighborhood codes. Compared with traditional methods, the principle of the uniform tile hierarchy is simple and easy to understand, the algorithm complexity is low, and the computer execution efficiency is high. The results of the experiments indicate that interoperation between geographic coordinates and codes efficiency of the proposed algorithm is approximately 2.74 and 1.73 times that of the traditional method respectively and that neighborhood codes query efficiency of the proposed algorithm is approximately 7.46 times that of the traditional method. As the grid level rises, the advantages of the proposed method become more obvious. The research results of this paper are expected to provide theoretical and technical supports for the unified organization, management, processing and analysis of multi-source Earth observation data.
, Available online ,
doi: 10.13203/j.whugis20200023
Abstract:
Buildings can be divided into physical cluster and property right cluster from different perspectives, and the latter attached to the former. The existing method can construct physical cluster and property right cluster of the same building automatically, but the two resulting clusters are independent of each other. This not only increases the cost of modeling, but also is not conducive to the update and maintenance of model data in the later period. For the problem, the paper studies the relationship between physical cluster and property right cluster of apartment buildings, and finds that the hierarchy of connected boundaries determines the property right solids aggregated by cells, and presents a method to transform physical clustering into property right clustering automatically. With existing physical clusters, the method transforms the cells of physical clusters into dual points, and the connected boundaries between cells into semantic edges, and the whole physical cluster into Node Relation Graph with Poincaré duality transformation. A segmenting algorithm is designed for Node Relation Graph, which can divide Node Relation Graph into sub Node Relation Graph representing proprietary and co-owned property according to the semantic information of the edges. Furthermore, the non-common boundary surfaces of the cell set corresponded by sub Node Relation Graph are extracted to construct the property right solids, and the aggregation of the property right solids forms the property right cluster. Instead of building two separate clusters, the method only builds a physical cluster and property right cluster is generated by the transformation. The results show that the proposed method can identify the property right solids and construct property right cluster in the existing physical cluster automatically. It saves the modeling cost and facilitates the update and maintenance of the later data.
Buildings can be divided into physical cluster and property right cluster from different perspectives, and the latter attached to the former. The existing method can construct physical cluster and property right cluster of the same building automatically, but the two resulting clusters are independent of each other. This not only increases the cost of modeling, but also is not conducive to the update and maintenance of model data in the later period. For the problem, the paper studies the relationship between physical cluster and property right cluster of apartment buildings, and finds that the hierarchy of connected boundaries determines the property right solids aggregated by cells, and presents a method to transform physical clustering into property right clustering automatically. With existing physical clusters, the method transforms the cells of physical clusters into dual points, and the connected boundaries between cells into semantic edges, and the whole physical cluster into Node Relation Graph with Poincaré duality transformation. A segmenting algorithm is designed for Node Relation Graph, which can divide Node Relation Graph into sub Node Relation Graph representing proprietary and co-owned property according to the semantic information of the edges. Furthermore, the non-common boundary surfaces of the cell set corresponded by sub Node Relation Graph are extracted to construct the property right solids, and the aggregation of the property right solids forms the property right cluster. Instead of building two separate clusters, the method only builds a physical cluster and property right cluster is generated by the transformation. The results show that the proposed method can identify the property right solids and construct property right cluster in the existing physical cluster automatically. It saves the modeling cost and facilitates the update and maintenance of the later data.
, Available online ,
doi: 10.13203/j.whugis20200633
Abstract:
Objectives: National park is the main body of China's natural protected area system, and land cover mapping in national parks plays an important role in understanding the status of natural resources, identifying existing ecological security threats and responding to them quickly. This analysis attempts to develop an accurate and cost-effective model for mapping land cover dataset in national parks. Methods: Two land cover classification methods were developed based on Google Earth Engine (GEE) environment by combining Sentinel-1 and Sentinel-2 images, topographic and textural derivatives to map land cover types in the Qianjiangyuan National Park. One used pixel-based random forest (RF) classification algorithm, the other used object-oriented simple non-iterative clustering (SNIC) segmentation in partnership with RF algorithm, which indicated that the input featureswere first segmented into superpixel image objects then classified by RF algorithm. To optimize classification features, a method taking the advantages of cloud computing platform to design different groups of comparative experiments to finalize feature combination with the highest classification accuracy was proposed, followed by using a recursive feature elimination method to further screen the features. Results: Experimental results showed that both the pixel-based method and the object-oriented method had good performance in land cover classification, and the corresponding highest overall classification accuracies of the two methods were estimated at 92.37% and 93.98%, respectively. Furthermore, integration of synthetic aperture radar (SAR) data into the classifications could substantially improve the classification accuracy when using the pixel-based method, but there was no apparent escalating effect for the object-oriented classification. Conclusions: Experiments showed that the land cover classification map generated from the SNIC+RF algorithm in GEE platform was more complete and the algorithm requires fewer features (15 features, including multispectral bands and spectral indices) and runs quickly in the GEE platform. Thus, this algorithm deserves to be popularized in national park management practices.
Objectives: National park is the main body of China's natural protected area system, and land cover mapping in national parks plays an important role in understanding the status of natural resources, identifying existing ecological security threats and responding to them quickly. This analysis attempts to develop an accurate and cost-effective model for mapping land cover dataset in national parks. Methods: Two land cover classification methods were developed based on Google Earth Engine (GEE) environment by combining Sentinel-1 and Sentinel-2 images, topographic and textural derivatives to map land cover types in the Qianjiangyuan National Park. One used pixel-based random forest (RF) classification algorithm, the other used object-oriented simple non-iterative clustering (SNIC) segmentation in partnership with RF algorithm, which indicated that the input featureswere first segmented into superpixel image objects then classified by RF algorithm. To optimize classification features, a method taking the advantages of cloud computing platform to design different groups of comparative experiments to finalize feature combination with the highest classification accuracy was proposed, followed by using a recursive feature elimination method to further screen the features. Results: Experimental results showed that both the pixel-based method and the object-oriented method had good performance in land cover classification, and the corresponding highest overall classification accuracies of the two methods were estimated at 92.37% and 93.98%, respectively. Furthermore, integration of synthetic aperture radar (SAR) data into the classifications could substantially improve the classification accuracy when using the pixel-based method, but there was no apparent escalating effect for the object-oriented classification. Conclusions: Experiments showed that the land cover classification map generated from the SNIC+RF algorithm in GEE platform was more complete and the algorithm requires fewer features (15 features, including multispectral bands and spectral indices) and runs quickly in the GEE platform. Thus, this algorithm deserves to be popularized in national park management practices.
, Available online ,
doi: 10.13203/j.whugis20200479
Abstract:
The differential code biases have a great influence on the estimation of ionosphere with GPS observations, which should be precisely calibrated when obtaining ionospheric slant total electron content (STEC). So far, the estimation of GPS satellite DCB is mainly based on the ground-based GPS observation data. With the increasing number of low-orbit satellites, DCB estimation of LEO receivers becomes particularly important for the topside ionosphere research. In this study, onboard observations of GPS satellites by the Swarm constellation are applied to estimate GPS DCBs and receiver DCBs. Since there are three satellites in the constellation, two different estimation schemes are designed, individually estimation and combined estimation respectively. Compared with the individual estimation strategy, The combined estimation strategy achieves more stable satellite DCB with a stability improv ement of 16.6% for GPS satellites, and it's satellite DCB presents better consistency with the reference DCB provi ded by the two analysis centers.
The differential code biases have a great influence on the estimation of ionosphere with GPS observations, which should be precisely calibrated when obtaining ionospheric slant total electron content (STEC). So far, the estimation of GPS satellite DCB is mainly based on the ground-based GPS observation data. With the increasing number of low-orbit satellites, DCB estimation of LEO receivers becomes particularly important for the topside ionosphere research. In this study, onboard observations of GPS satellites by the Swarm constellation are applied to estimate GPS DCBs and receiver DCBs. Since there are three satellites in the constellation, two different estimation schemes are designed, individually estimation and combined estimation respectively. Compared with the individual estimation strategy, The combined estimation strategy achieves more stable satellite DCB with a stability improv ement of 16.6% for GPS satellites, and it's satellite DCB presents better consistency with the reference DCB provi ded by the two analysis centers.
, Available online ,
doi: 10.13203/j.whugis20200688
Abstract:
Backscatter coefficient (sigma0) is one of observations of satellite radar altimetry. It is related to the physical and geometric characteristics of land surface under the influence of global/regional climate change. Sigma0 can be used for monitoring surface features under climate change, data calibration and verification of satellite altimeters, inversion of surface features (e.g. soil moisture, snow thickness, etc.) and other fields. GlobeLand30, as the global land cover information with the resolution of 30 m, was produced in China. The Geophysical Data Record (GDR) data of Jason-2 was used to extract and isolate the Ku-band sigma0 data of the Tibetan Plateau (TP). By using the GlobeLand30 2020 version as the basis for surface classification, the latitude and longitude paired sigma0 data give surface attributes, and then we obtain the time-varying sequence of sigma0 under different types of surface features. The singular spectrum analysis (SSA) interpolation is used to fill in missing data. The sigma0 time change trend and period information of the entire TP and different surface attributes are extracted and identified with SSA, and the period results are analyzed by FFT. From the analysis of sigma0 under different surface attributes, its time-varying sequence has different characteristic results:(1) The sigma0 is higher in waters and wetland areas, and the sigma0 is lower in permanent snow and ice areas. (2) There are stable annual, semi-annual and quarterly signals for sigma0 in the TP. The surface properties of the artificial surfaces, bare land, and shrubland area are stable, and the annual sigma0 change is not significant. The changes of sigma0 in other regions have significant annual and semi-annual periods. The amplitude of the semi-annual and quarterly signal is varies with the nature of the surface. (3) The sigma0 changes in the TP show an increasing trend. It is caused by climate change on the TP and wet surface. The sigma0 data of forest, grassland, and shrubland have the increasing trend, the sigma0 of wetland has a trend of decreasing. Besides geophysics and ocean dynamics research, satellite radar altimeter is also feasible for monitoring land environment. The sigma0 obtained by altimeter is closely related to ground properties and climate change. The effects of different geographical attributes on sigma0 in the TP show different time-varying status:(1) the change cycle of sigma0 is mainly annual cycle, and different land surface states have different periodic attributes, which is related to the response state of different land surface to climate change; (2) The sigma0 value in water and wetland is significantly higher than other areas, which may be caused by the difference of surface complex dielectric constant.
Backscatter coefficient (sigma0) is one of observations of satellite radar altimetry. It is related to the physical and geometric characteristics of land surface under the influence of global/regional climate change. Sigma0 can be used for monitoring surface features under climate change, data calibration and verification of satellite altimeters, inversion of surface features (e.g. soil moisture, snow thickness, etc.) and other fields. GlobeLand30, as the global land cover information with the resolution of 30 m, was produced in China. The Geophysical Data Record (GDR) data of Jason-2 was used to extract and isolate the Ku-band sigma0 data of the Tibetan Plateau (TP). By using the GlobeLand30 2020 version as the basis for surface classification, the latitude and longitude paired sigma0 data give surface attributes, and then we obtain the time-varying sequence of sigma0 under different types of surface features. The singular spectrum analysis (SSA) interpolation is used to fill in missing data. The sigma0 time change trend and period information of the entire TP and different surface attributes are extracted and identified with SSA, and the period results are analyzed by FFT. From the analysis of sigma0 under different surface attributes, its time-varying sequence has different characteristic results:(1) The sigma0 is higher in waters and wetland areas, and the sigma0 is lower in permanent snow and ice areas. (2) There are stable annual, semi-annual and quarterly signals for sigma0 in the TP. The surface properties of the artificial surfaces, bare land, and shrubland area are stable, and the annual sigma0 change is not significant. The changes of sigma0 in other regions have significant annual and semi-annual periods. The amplitude of the semi-annual and quarterly signal is varies with the nature of the surface. (3) The sigma0 changes in the TP show an increasing trend. It is caused by climate change on the TP and wet surface. The sigma0 data of forest, grassland, and shrubland have the increasing trend, the sigma0 of wetland has a trend of decreasing. Besides geophysics and ocean dynamics research, satellite radar altimeter is also feasible for monitoring land environment. The sigma0 obtained by altimeter is closely related to ground properties and climate change. The effects of different geographical attributes on sigma0 in the TP show different time-varying status:(1) the change cycle of sigma0 is mainly annual cycle, and different land surface states have different periodic attributes, which is related to the response state of different land surface to climate change; (2) The sigma0 value in water and wetland is significantly higher than other areas, which may be caused by the difference of surface complex dielectric constant.
, Available online ,
doi: 10.13203/j.whugis20200699
Abstract:
Objectives: Terrain simplification, which uses minimal amount of effective terrain information to express the overall terrain, can solve the contradiction between massive terrain data and computer hardware, and meet the needs of multi-scale terrain applications. However, itis difficult for most of the existing terrain simplification algorithms to take local fluctuations and overall characteristics of the terrain into account at the same time. Aiming at these deficiencies, a terrain adaptive simplification algorithm based on centroidal Voronoi diagram is proposed. Methods: Centroid Voronoi diagram has the characteristic that site points converge to the region with larger density. Meanwhile, more data points should be assigned to the region with larger relief and rough surface in terrain simplification. Therefore, centroidal Voronoi diagram, which is generated by considering the topographic relief as density function, is used to simplify the terrain adaptively. Firstly, based on thedensity function given by topographic relief extracted from Digital Terrain Model (DEM), centroidal Voronoi diagram is generated using Lloyd's algorithm. Secondly, the DEM is simplified and reconstructed according to the sites of the centroidal Voronoi diagram that distributed in areas with large topographic relief, and Voronoi vertices, which mostly distributed on the terrain feature lines. Thirdly, the effect of simplification is verified qualitatively by contrasting the feature lines, such as collection waterlines, ridge and valley lines and contours, of the reconstructed and theoriginal terrain. Finally, Root Mean Square Errors (RMSE) of the proposed algorithm and 3D Douglas-Peucker algorithm are computed and contrasted to access the effect of the simplification quantitatively. Results: (1) Feature lines, such as collection waterlines, ridge and valley lines and contours, extracted from simplified terrain and the original terrain have high degree of overlap. Therefore, the simplified terrain maintains the features of the original terrain better due the use of Voronoi vertices mostly distributed on the terrain feature lines. (2) The RMSE of the proposed algorithm is lower than that of the 3D Douglas-Peucker algorithm at each level of simplification. This is mainly because the proposed algorithm takes both centroidal Voronoi sites that distributed in areas with large topographic relief and centroidal Voronoi vertices that mostly distributed on the terrain feature lines into consideration, while 3D Douglas-Peucker algorithm only preserves the points distributed on the ridge and valley lines. Conclusions: The proposed centroidal Voronoi diagram-based algorithm simplifies terrain according to points in areas with large topographic relief and points on the ridge and valley lines of terrain. Both local fluctuations and overall characteristics of the terrain are taken into consideration in the algorithm, so it has higher accuracy compared with 3D Douglas-Peucker algorithm while maintaining the overall features of the terrain.
Objectives: Terrain simplification, which uses minimal amount of effective terrain information to express the overall terrain, can solve the contradiction between massive terrain data and computer hardware, and meet the needs of multi-scale terrain applications. However, itis difficult for most of the existing terrain simplification algorithms to take local fluctuations and overall characteristics of the terrain into account at the same time. Aiming at these deficiencies, a terrain adaptive simplification algorithm based on centroidal Voronoi diagram is proposed. Methods: Centroid Voronoi diagram has the characteristic that site points converge to the region with larger density. Meanwhile, more data points should be assigned to the region with larger relief and rough surface in terrain simplification. Therefore, centroidal Voronoi diagram, which is generated by considering the topographic relief as density function, is used to simplify the terrain adaptively. Firstly, based on thedensity function given by topographic relief extracted from Digital Terrain Model (DEM), centroidal Voronoi diagram is generated using Lloyd's algorithm. Secondly, the DEM is simplified and reconstructed according to the sites of the centroidal Voronoi diagram that distributed in areas with large topographic relief, and Voronoi vertices, which mostly distributed on the terrain feature lines. Thirdly, the effect of simplification is verified qualitatively by contrasting the feature lines, such as collection waterlines, ridge and valley lines and contours, of the reconstructed and theoriginal terrain. Finally, Root Mean Square Errors (RMSE) of the proposed algorithm and 3D Douglas-Peucker algorithm are computed and contrasted to access the effect of the simplification quantitatively. Results: (1) Feature lines, such as collection waterlines, ridge and valley lines and contours, extracted from simplified terrain and the original terrain have high degree of overlap. Therefore, the simplified terrain maintains the features of the original terrain better due the use of Voronoi vertices mostly distributed on the terrain feature lines. (2) The RMSE of the proposed algorithm is lower than that of the 3D Douglas-Peucker algorithm at each level of simplification. This is mainly because the proposed algorithm takes both centroidal Voronoi sites that distributed in areas with large topographic relief and centroidal Voronoi vertices that mostly distributed on the terrain feature lines into consideration, while 3D Douglas-Peucker algorithm only preserves the points distributed on the ridge and valley lines. Conclusions: The proposed centroidal Voronoi diagram-based algorithm simplifies terrain according to points in areas with large topographic relief and points on the ridge and valley lines of terrain. Both local fluctuations and overall characteristics of the terrain are taken into consideration in the algorithm, so it has higher accuracy compared with 3D Douglas-Peucker algorithm while maintaining the overall features of the terrain.
, Available online ,
doi: 10.13203/j.whugis20200328
Abstract:
Sampling inspection is a fundamental approach in quality management and control of surveying and mapping achievements. Sampling scheme,which includes sampling plan and lot-judging rules, is the critical contents of sampling inspection as it directly determine the result of quality inspection. This paper focuses on the sampling schemes and investigates the validity and suitability of sampling inspection by attributes. A new conception of QUI (Quality Uncertain Interval), which is used to descript sampling scheme's characters, is constructed based on the theory of probability and hypothesis test. Because one sampling scheme can only determinate one QUI, both the QUI length and the quality limits of TYPE I and Ⅱ errors are used to evaluate the effectiveness and validity of sampling schemes. Four typical sampling schemes adopted in GB/T24356-2009 and six simulated sampling schemes are selected to test and analyze. Numerical results show QUI is rational and appropriate for inspection risk controlling of both Geographic products and all kinds of attributes-based sampling industrial products.
Sampling inspection is a fundamental approach in quality management and control of surveying and mapping achievements. Sampling scheme,which includes sampling plan and lot-judging rules, is the critical contents of sampling inspection as it directly determine the result of quality inspection. This paper focuses on the sampling schemes and investigates the validity and suitability of sampling inspection by attributes. A new conception of QUI (Quality Uncertain Interval), which is used to descript sampling scheme's characters, is constructed based on the theory of probability and hypothesis test. Because one sampling scheme can only determinate one QUI, both the QUI length and the quality limits of TYPE I and Ⅱ errors are used to evaluate the effectiveness and validity of sampling schemes. Four typical sampling schemes adopted in GB/T24356-2009 and six simulated sampling schemes are selected to test and analyze. Numerical results show QUI is rational and appropriate for inspection risk controlling of both Geographic products and all kinds of attributes-based sampling industrial products.
, Available online ,
doi: 10.13203/j.whugis20210045
Abstract:
Objectives: The structure of the tourism flow network is of great significance for understanding the choices of tourists and the role of attractions in the network. The previous studies mainly focused on the structure of the tourism flow network of all tourists. However, the analysis on the disparities of tourism flow network for different tourists is still lack in the thorough research. Therefore, we analyzed the tourism flow networks constructed by different types of tourism routes from online travel notes. Methods: Based on the online travel notes, the text mining and social network analysis methods are used to construct and analyze tourism flow networks. First, we use text mining to extract the multi-dimensional preferences of tourists, and cluster tourists into different groups. Second, the destination sequences of different tourist groups are used to construct various tourist flow networks. Finally, the structural characteristics of these tourism flow networks and the role of each destination node are analyzed from multiple perspectives. Results: The experiment takes Yunnan Province as the case study area, and the tourists travelled in Yunnan in 2019 are clustered into five groups, then five travel flow networks are constructed. The results show that:(1) The tourism flow network structures of five clusters of tourists are distinct, demonstrating the disparities of spatial interaction patterns among travel destinations and different degrees of network centralization; (2) The travel destinations of cost sensitive and time sensitive tourists are primarily a few popular attractions and some attractions around them. The networks of these two types of tourists show a single-core structure. As for other types of tourists, their travel destinations are more diverse and their travel routes have a larger spatial span. The networks of these types of tourists present a typical multi-core structure. (3) Some travel destinations like Lugu Lake, Xizhou and Dian Lake take opposite roles in the tourism flow networks of different clusters of tourists. Conclusions: Our research is helpful for tourism management department to clarify the characteristics of tourism flows and optimize the cooperation mechanism of travel destinations in the tourism network. In the future work, we will focus on exploring the influencing factors of different tourism flow network characteristics, and applying the results to personalized tourism route recommendations.
Objectives: The structure of the tourism flow network is of great significance for understanding the choices of tourists and the role of attractions in the network. The previous studies mainly focused on the structure of the tourism flow network of all tourists. However, the analysis on the disparities of tourism flow network for different tourists is still lack in the thorough research. Therefore, we analyzed the tourism flow networks constructed by different types of tourism routes from online travel notes. Methods: Based on the online travel notes, the text mining and social network analysis methods are used to construct and analyze tourism flow networks. First, we use text mining to extract the multi-dimensional preferences of tourists, and cluster tourists into different groups. Second, the destination sequences of different tourist groups are used to construct various tourist flow networks. Finally, the structural characteristics of these tourism flow networks and the role of each destination node are analyzed from multiple perspectives. Results: The experiment takes Yunnan Province as the case study area, and the tourists travelled in Yunnan in 2019 are clustered into five groups, then five travel flow networks are constructed. The results show that:(1) The tourism flow network structures of five clusters of tourists are distinct, demonstrating the disparities of spatial interaction patterns among travel destinations and different degrees of network centralization; (2) The travel destinations of cost sensitive and time sensitive tourists are primarily a few popular attractions and some attractions around them. The networks of these two types of tourists show a single-core structure. As for other types of tourists, their travel destinations are more diverse and their travel routes have a larger spatial span. The networks of these types of tourists present a typical multi-core structure. (3) Some travel destinations like Lugu Lake, Xizhou and Dian Lake take opposite roles in the tourism flow networks of different clusters of tourists. Conclusions: Our research is helpful for tourism management department to clarify the characteristics of tourism flows and optimize the cooperation mechanism of travel destinations in the tourism network. In the future work, we will focus on exploring the influencing factors of different tourism flow network characteristics, and applying the results to personalized tourism route recommendations.
, Available online ,
doi: 10.13203/j.whugis20200510
Abstract:
Objectives: Spectral analysis is to analyze and study various spectra of potential anomalies with wave-number as the independent variable, to solve the problems of anomaly conversion, filtering and forward and inverse analysis in practical work. The spectral characteristics of the potential include the spatial distribution, lithology and tectonic characteristics of the subsurface stratum, which helps the interpretation and analysis of geophysical data and provides information for further understanding of the study area. The amplitude spectrum of the potential anomaly spectrum is directly related to the burial depth, width of the subsurface geological body; the phase spectrum reflects the information of the horizontal position and dip angle of the subsurface geological body. However, in previous studies, most of the researchers' work had been focused on making use of the characteristics of fast calculation of the bitfield data in the frequency domain, doing a lot of forward work as well as applying the relationship between the spectrum and the parameters of the geologic body for inversion, but the analysis of the spectral characteristics of different geologic bodies and the specific effects of the geologic body parameters on the spectrum have been less studied. On the other hand, there are few studies on the joint inversion using different spectra in the frequency domain inversion. Methods: In order to better carry out the study of gravity anomaly in the frequency domain, using the characteristics of simple, solvable and easy to program and fast computation of the expression for the positive inversion of gravity anomaly in the frequency domain, in this paper, the spectral curves of various models are calculated, and the characteristics of the spectral curves, and details the effects of model parameter changes on the spectral curves are analyzed too; it also includes how to interpret the amplitude spectral inversion and phase spectral inversion, and proposes a joint inversion method in the frequency domain to accurately invert the geometric parameters of the models. Results: The gravitational anomaly spectrum of a horizontal cylinder, which is of the periodic fluctuation type with monotonically decreasing amplitude spectrum and its phase spectrum is a straight line with a negative slope; the gravity anomaly spectrum of the tilted thin plate is periodically fluctuating, with monotonically decreasing amplitude spectrum and fluctuating phase spectrum; the gravity anomaly spectrum of the tilted thick plate is periodically fluctuating with fluctuating amplitude spectrum and fluctuating and progressive phase spectrum. By analyzing the influence of different parameter changes of the thick plate on the model spectrum, the trend of the subsurface geological body is judged according to the spectrum changes. The amplitude spectrum can determine the burial depth of the model, and the change of its first zero value point position is determined in the horizontal direction of the model, and the phase spectrum is directly related to the model parameters. The joint inversion in frequency domain proposed in this paper is an effective method to combine the advantages of amplitude spectrum inversion and phase spectrum inversion to accurately calculate the geometric parameters of the model. The method was applied to the inversion of the target stratigraphy in the Wuqing Sag, and the precise depth and width of the target substratum in the two measured sections were obtained, providing a basis for further seismic studies in the area.
Objectives: Spectral analysis is to analyze and study various spectra of potential anomalies with wave-number as the independent variable, to solve the problems of anomaly conversion, filtering and forward and inverse analysis in practical work. The spectral characteristics of the potential include the spatial distribution, lithology and tectonic characteristics of the subsurface stratum, which helps the interpretation and analysis of geophysical data and provides information for further understanding of the study area. The amplitude spectrum of the potential anomaly spectrum is directly related to the burial depth, width of the subsurface geological body; the phase spectrum reflects the information of the horizontal position and dip angle of the subsurface geological body. However, in previous studies, most of the researchers' work had been focused on making use of the characteristics of fast calculation of the bitfield data in the frequency domain, doing a lot of forward work as well as applying the relationship between the spectrum and the parameters of the geologic body for inversion, but the analysis of the spectral characteristics of different geologic bodies and the specific effects of the geologic body parameters on the spectrum have been less studied. On the other hand, there are few studies on the joint inversion using different spectra in the frequency domain inversion. Methods: In order to better carry out the study of gravity anomaly in the frequency domain, using the characteristics of simple, solvable and easy to program and fast computation of the expression for the positive inversion of gravity anomaly in the frequency domain, in this paper, the spectral curves of various models are calculated, and the characteristics of the spectral curves, and details the effects of model parameter changes on the spectral curves are analyzed too; it also includes how to interpret the amplitude spectral inversion and phase spectral inversion, and proposes a joint inversion method in the frequency domain to accurately invert the geometric parameters of the models. Results: The gravitational anomaly spectrum of a horizontal cylinder, which is of the periodic fluctuation type with monotonically decreasing amplitude spectrum and its phase spectrum is a straight line with a negative slope; the gravity anomaly spectrum of the tilted thin plate is periodically fluctuating, with monotonically decreasing amplitude spectrum and fluctuating phase spectrum; the gravity anomaly spectrum of the tilted thick plate is periodically fluctuating with fluctuating amplitude spectrum and fluctuating and progressive phase spectrum. By analyzing the influence of different parameter changes of the thick plate on the model spectrum, the trend of the subsurface geological body is judged according to the spectrum changes. The amplitude spectrum can determine the burial depth of the model, and the change of its first zero value point position is determined in the horizontal direction of the model, and the phase spectrum is directly related to the model parameters. The joint inversion in frequency domain proposed in this paper is an effective method to combine the advantages of amplitude spectrum inversion and phase spectrum inversion to accurately calculate the geometric parameters of the model. The method was applied to the inversion of the target stratigraphy in the Wuqing Sag, and the precise depth and width of the target substratum in the two measured sections were obtained, providing a basis for further seismic studies in the area.
, Available online ,
doi: 10.13203/j.whugis20200281
Abstract:
Compared with the traditional single-polarization SAR, the full-polarization SAR can obtain more abundant polarimetric scattering information and describe the geometric and physical characteristics of the target more comprehensively. To make full use of the polarimetric scattering information of ground objects, the paper utilizes the polarimetric coherence matrix used to describe distributed targets and the polarimetric likelihood ratio test (PolLRT) based on complex Wishart distribution to accurately evaluate the temporal similarity between master and slave image blocks. Compared with the traditional method, the method not only considers the cross-correlation information between the same polarization, but also considers the cross-correlation information between different polarization modes, to improve the matching performance of the time series polarization information. Finally, in the real experiment, two fully polarized Unmanned Aerial Vehicle SAR (UAVSAR) data are used as the experimental data, and the external global positioning system (GPS) deformation data is used as the reference data. The experimental results show that the proposed algorithm has higher deformation extraction accuracy and shows more robust deformation extraction performance under different matching window sizes.
Compared with the traditional single-polarization SAR, the full-polarization SAR can obtain more abundant polarimetric scattering information and describe the geometric and physical characteristics of the target more comprehensively. To make full use of the polarimetric scattering information of ground objects, the paper utilizes the polarimetric coherence matrix used to describe distributed targets and the polarimetric likelihood ratio test (PolLRT) based on complex Wishart distribution to accurately evaluate the temporal similarity between master and slave image blocks. Compared with the traditional method, the method not only considers the cross-correlation information between the same polarization, but also considers the cross-correlation information between different polarization modes, to improve the matching performance of the time series polarization information. Finally, in the real experiment, two fully polarized Unmanned Aerial Vehicle SAR (UAVSAR) data are used as the experimental data, and the external global positioning system (GPS) deformation data is used as the reference data. The experimental results show that the proposed algorithm has higher deformation extraction accuracy and shows more robust deformation extraction performance under different matching window sizes.
, Available online ,
doi: 10.13203/j.whugis20200382
Abstract:
To explore the relationship between urban ecological environment and human activities is an important research content in the current urbanization process. With the in-depth development of the era of big data, multi-source data ubiquitous in the Internet has been fully excavated, which has played an important role in promoting the research of urban ecological environment.Based on multi-source data, the paper proposed toconstruct human activity indicators (residential area walkability index, street vitality index, urban function mixing index) by using Point of Interests (POIs), Open Street Map (OSM) and residential area data, and urban ecological environment indicator (remote sensing ecological index) by using remote sensing images. Combing machine learning models such as polynomial regression(PLR), random forest regression(RFR), eXtreme Gradient Boosting Regression(XGB) and support vector regression machine(SVR), it is effective to make regression analysis on urban ecological environment and human activity indicators.By comparing the performance of different models in this data set, the relationship between the urban ecological environment and human activities is revealed.We demonstrate theapplication of our method using a case study of Nanchang city. The results show that:①The three indexes of human activities all present a central high and gradually decrease to the surroundings, while the urban ecological environment indicators show an opposite trend.②From the analysis of the performance results of each model on the data set, XGB has the best regression effect, followed by PLR.③There is a strong negative correlation between the urban ecological environment and human activities, and the street vitality index, the urban function mixing index are more relevant to the urban ecological environment, and the walkability index of the residential area is less relevant to the urban ecological environment.④In areas where human activities have less impact, the urban ecological environment will be disturbed by other factors, resulting in the low prediction accuracy, while the prediction accuracy in areas with strong human activities is high.
To explore the relationship between urban ecological environment and human activities is an important research content in the current urbanization process. With the in-depth development of the era of big data, multi-source data ubiquitous in the Internet has been fully excavated, which has played an important role in promoting the research of urban ecological environment.Based on multi-source data, the paper proposed toconstruct human activity indicators (residential area walkability index, street vitality index, urban function mixing index) by using Point of Interests (POIs), Open Street Map (OSM) and residential area data, and urban ecological environment indicator (remote sensing ecological index) by using remote sensing images. Combing machine learning models such as polynomial regression(PLR), random forest regression(RFR), eXtreme Gradient Boosting Regression(XGB) and support vector regression machine(SVR), it is effective to make regression analysis on urban ecological environment and human activity indicators.By comparing the performance of different models in this data set, the relationship between the urban ecological environment and human activities is revealed.We demonstrate theapplication of our method using a case study of Nanchang city. The results show that:①The three indexes of human activities all present a central high and gradually decrease to the surroundings, while the urban ecological environment indicators show an opposite trend.②From the analysis of the performance results of each model on the data set, XGB has the best regression effect, followed by PLR.③There is a strong negative correlation between the urban ecological environment and human activities, and the street vitality index, the urban function mixing index are more relevant to the urban ecological environment, and the walkability index of the residential area is less relevant to the urban ecological environment.④In areas where human activities have less impact, the urban ecological environment will be disturbed by other factors, resulting in the low prediction accuracy, while the prediction accuracy in areas with strong human activities is high.
, Available online ,
doi: 10.13203/j.whugis20210184
Abstract:
The high precision registration of point cloud data is the key to ensure the integrity of the three-dimensional data on the surface of space objects. Aiming at the problem of the position, posture and scale difference of the cloud data of adjacent stations, a new registration method using dual quaternion description under the constraint of point and surface features is proposed. First, the dual quaternion are used to represent the rotation matrix and the translation vector of the space similarity transformation, on this basis, the scale factor is taken into account. There is a vertical and parallel spatial topological relationship between the vector constructed according to the point in the plane and the point out the plane and the normal vector of the plane, and use this as the constraint condition of the spatial similarity transformation. The adjustment model is constructed based on the least square rule. Then, the Levenberg-Marquardt method is introduced to solve the adjustment model to avoid the iterative non-convergence caused by the improper initial value or the symmetric matrices constructed by the Jacobian matrix is close to singularity. Finally, through a comparative analysis of two sets of experiments and existing methods. The experimental results show that the method that takes into account the scale factor and uses the dual quaternion to achieve spatial similarity transformation under the constraints of point and surface features has strong practical value.
The high precision registration of point cloud data is the key to ensure the integrity of the three-dimensional data on the surface of space objects. Aiming at the problem of the position, posture and scale difference of the cloud data of adjacent stations, a new registration method using dual quaternion description under the constraint of point and surface features is proposed. First, the dual quaternion are used to represent the rotation matrix and the translation vector of the space similarity transformation, on this basis, the scale factor is taken into account. There is a vertical and parallel spatial topological relationship between the vector constructed according to the point in the plane and the point out the plane and the normal vector of the plane, and use this as the constraint condition of the spatial similarity transformation. The adjustment model is constructed based on the least square rule. Then, the Levenberg-Marquardt method is introduced to solve the adjustment model to avoid the iterative non-convergence caused by the improper initial value or the symmetric matrices constructed by the Jacobian matrix is close to singularity. Finally, through a comparative analysis of two sets of experiments and existing methods. The experimental results show that the method that takes into account the scale factor and uses the dual quaternion to achieve spatial similarity transformation under the constraints of point and surface features has strong practical value.
, Available online ,
doi: 10.13203/j.whugis20200601
Abstract:
The research on estuarine topography and landform is great significance to human social and economic activities such as river management and construction, water transportation and so on. The establishment of an accurate and seamless chart datum and its transformation model with other vertical datum will directly affect the acquisition of high-precision topographic and geomorphic data and the unified comprehensive management and analysis. In view of this, this paper studies the methods of establishment of seamless chart datum and its transformation model with other vertical datum based on the 3 dimensional numerical simulation of tidal wave motion, sea surface topography and geoid. First, the 3D tidal motion was simulated by using FVCOM, and seamless chart datum was built on this basis. Then, the mean sea level model was built with combing the EGM2008 geoid model and mean sea surface topography model released by Copernicus Marine Environment Monitoring Service (CMEMS). Finally, the transformation model between seamless chart datum and ellipsoid reference had been established through combing the seamless chart datum and the mean level model. We have carried out the experiment which is located at the typical estuarine waters-Yangtze Estuary south branch. Subsequently, a transformation model has been established and evaluated accuracy. The results show that the mean square error of vertical datum transformation model is 12.4 cm, and the mean of absolute value of difference between transformation model estimates and field observation at long-term tidal stations is 24.2 cm. The main reason for this large difference between the accuracy evaluation and the actual comparison value is due to the poor accuracy of mean sea surface topography. Especially in the nearshore waters, the accuracy of sea surface topography obtained by satellite altimetry is far worse than that in the far-sea waters. Although it is larger than the mean square error of model, it is still considerably smaller than the maximum allowable total vertical uncertainty of the charted depth specified by the International Hydrographic Organization. Therefore, the researches of this paper are feasible and reliable, especially in the far-sea area. When the accuracy of satellite altimetry in the nearshore is further improved, the methods of this paper can be extended to nearshore waters and used widely.
The research on estuarine topography and landform is great significance to human social and economic activities such as river management and construction, water transportation and so on. The establishment of an accurate and seamless chart datum and its transformation model with other vertical datum will directly affect the acquisition of high-precision topographic and geomorphic data and the unified comprehensive management and analysis. In view of this, this paper studies the methods of establishment of seamless chart datum and its transformation model with other vertical datum based on the 3 dimensional numerical simulation of tidal wave motion, sea surface topography and geoid. First, the 3D tidal motion was simulated by using FVCOM, and seamless chart datum was built on this basis. Then, the mean sea level model was built with combing the EGM2008 geoid model and mean sea surface topography model released by Copernicus Marine Environment Monitoring Service (CMEMS). Finally, the transformation model between seamless chart datum and ellipsoid reference had been established through combing the seamless chart datum and the mean level model. We have carried out the experiment which is located at the typical estuarine waters-Yangtze Estuary south branch. Subsequently, a transformation model has been established and evaluated accuracy. The results show that the mean square error of vertical datum transformation model is 12.4 cm, and the mean of absolute value of difference between transformation model estimates and field observation at long-term tidal stations is 24.2 cm. The main reason for this large difference between the accuracy evaluation and the actual comparison value is due to the poor accuracy of mean sea surface topography. Especially in the nearshore waters, the accuracy of sea surface topography obtained by satellite altimetry is far worse than that in the far-sea waters. Although it is larger than the mean square error of model, it is still considerably smaller than the maximum allowable total vertical uncertainty of the charted depth specified by the International Hydrographic Organization. Therefore, the researches of this paper are feasible and reliable, especially in the far-sea area. When the accuracy of satellite altimetry in the nearshore is further improved, the methods of this paper can be extended to nearshore waters and used widely.
, Available online ,
doi: 10.13203/j.whugis20200165
Abstract:
Water body plays an extremely important role in regional climate regulation. In recent decades,due to the unreasonable utilization and intensive development of the Aral Sea water resources by human beings, the water level and area of the Aral Sea have experienced a sharp decline and shrinkage, which inevitably has an important impact on the regional climate of the Aral Sea. So analyzing the changes of surface temperature variation in the Aral Sea region is of great significance for studying the relationship between the water body and the regional surface temperature. In order to reveal the variation characteristics of surface temperature affected by the Aral Sea, the MODIS surface temperature product (MOD11A2) in the Aral Sea area from 2000 to 2015 was used to analyze the affection on the regional surface temperature by the Area Sea based on the spatial analysis method. The results show that:(1) Compared with the surface temperature of the Aral Sea, 93% and 69% of the study area showed an upward trend respectively in the daytime and nighttime during the study period. (2) The direct impact range that the Aral Sea on the regional surface temperature is roughly within 30 km along the lake boundary. (3) As the area of the Aral Sea shrinks rapidly, the surface temperature difference between the various land cover types and the Aral Sea shows an increasing change trend, and the regional temperature regulation function of the Aral Sea gradually weakens.
Water body plays an extremely important role in regional climate regulation. In recent decades,due to the unreasonable utilization and intensive development of the Aral Sea water resources by human beings, the water level and area of the Aral Sea have experienced a sharp decline and shrinkage, which inevitably has an important impact on the regional climate of the Aral Sea. So analyzing the changes of surface temperature variation in the Aral Sea region is of great significance for studying the relationship between the water body and the regional surface temperature. In order to reveal the variation characteristics of surface temperature affected by the Aral Sea, the MODIS surface temperature product (MOD11A2) in the Aral Sea area from 2000 to 2015 was used to analyze the affection on the regional surface temperature by the Area Sea based on the spatial analysis method. The results show that:(1) Compared with the surface temperature of the Aral Sea, 93% and 69% of the study area showed an upward trend respectively in the daytime and nighttime during the study period. (2) The direct impact range that the Aral Sea on the regional surface temperature is roughly within 30 km along the lake boundary. (3) As the area of the Aral Sea shrinks rapidly, the surface temperature difference between the various land cover types and the Aral Sea shows an increasing change trend, and the regional temperature regulation function of the Aral Sea gradually weakens.
, Available online ,
doi: 10.13203/j.whugis20200142
Abstract:
High-precision precipitation field is important data support for hydrological, meteorological, and environmental analysis, which directly affects the accuracy of relevant services. The traditional simulation of precipitation distribution mostly relies on the spatial driving factors but neglects the impact of the temporal characteristics of precipitation on its spatial distribution. This work employed the average cumulative precipitation data from 83 national meteorological stations and 28 provincial meteorological stations in Hubei Province from 2015 to 2017. According to the correlation analysis, the arch height (C) and nugget value (C0) of the theoretical semivariogram model of precipitation time series are introduced as the influencing factors. This work applies the Geographically Weighted Regression (GWR) model to simulate the monthly average precipitation distribution model of Hubei Province. The results show that:1) The time-series semivariogram curve of monthly precipitation at each station is consistent with the seasonality of precipitation. 25.3% of the theoretical variation function model of site time series become stable in 4 months, and 36.14% in 6 months. 2) Arch height (C) and nugget value (C0) of the theoretical variation function model of precipitation time series at each station are correlated with the annual average cumulative precipitation in December of 0.01 level (both sides). The average correlation coefficients are 0.745 and 0.526, which are greater than the influence of geographical location and elevation on precipitation.3) The introduction of C and C0 is helpful to improve the global goodness of fit and interpolation accuracy of GWR model. By comparing the GWR model with latitude and longitude and the GWR model with variation function features of time series theory, the three-year average overall goodness of fit increased from 0.852 to 0.912. The accuracy evaluation of interpolation in the verification set site showed that the Mean Absolute Error(MAE), the Root Mean Square Error(RMSE) and the Mean Absolute Percentage Error(MAPE) all declined by more than 60% in three years. Therefore, the spatial-temporal weighted regression model with time-series theory variational function features can obtain high precision precipitation simulation results and is more suitable for the estimation of precipitation spatial distribution in the regions with abundant historical precipitation data.
High-precision precipitation field is important data support for hydrological, meteorological, and environmental analysis, which directly affects the accuracy of relevant services. The traditional simulation of precipitation distribution mostly relies on the spatial driving factors but neglects the impact of the temporal characteristics of precipitation on its spatial distribution. This work employed the average cumulative precipitation data from 83 national meteorological stations and 28 provincial meteorological stations in Hubei Province from 2015 to 2017. According to the correlation analysis, the arch height (C) and nugget value (C0) of the theoretical semivariogram model of precipitation time series are introduced as the influencing factors. This work applies the Geographically Weighted Regression (GWR) model to simulate the monthly average precipitation distribution model of Hubei Province. The results show that:1) The time-series semivariogram curve of monthly precipitation at each station is consistent with the seasonality of precipitation. 25.3% of the theoretical variation function model of site time series become stable in 4 months, and 36.14% in 6 months. 2) Arch height (C) and nugget value (C0) of the theoretical variation function model of precipitation time series at each station are correlated with the annual average cumulative precipitation in December of 0.01 level (both sides). The average correlation coefficients are 0.745 and 0.526, which are greater than the influence of geographical location and elevation on precipitation.3) The introduction of C and C0 is helpful to improve the global goodness of fit and interpolation accuracy of GWR model. By comparing the GWR model with latitude and longitude and the GWR model with variation function features of time series theory, the three-year average overall goodness of fit increased from 0.852 to 0.912. The accuracy evaluation of interpolation in the verification set site showed that the Mean Absolute Error(MAE), the Root Mean Square Error(RMSE) and the Mean Absolute Percentage Error(MAPE) all declined by more than 60% in three years. Therefore, the spatial-temporal weighted regression model with time-series theory variational function features can obtain high precision precipitation simulation results and is more suitable for the estimation of precipitation spatial distribution in the regions with abundant historical precipitation data.
, Available online ,
doi: 10.13203/j.whugis20200216
Abstract:
When using variance component estimation to determine the relative weight ratio, the least square solution is generally used as the initial value of the iteration. In geodetic joint inversion, the least squares method will cause ill-posed problems, so the regularized solution is used instead of the least squares solution. Regularization introduces bias to reduce variance, but when using variance component estimation to determine the relative weight ratio, the influence of bias is not considered, and the introduction of bias will cause inaccurate variance component estimation. This paper adopts the bias-corrected variance component estimation method to eliminate the influence of bias introduced by regularization. Simultaneously, the method in this paper, the residual-based bias-corrected variance component estimation and the variance component estimation method are used for simulation experiments. The experiment shows that the variance component estimation method after bias correction can better reverse the slip distribution. The bias-corrected variance component estimation method takes into account the bias introduced by the iterative initial value, and the theory is more rigorous. The Visso earthquake and Norcia earthquake are used for verification, the results show that the bias correction is reasonable and advantageous.
When using variance component estimation to determine the relative weight ratio, the least square solution is generally used as the initial value of the iteration. In geodetic joint inversion, the least squares method will cause ill-posed problems, so the regularized solution is used instead of the least squares solution. Regularization introduces bias to reduce variance, but when using variance component estimation to determine the relative weight ratio, the influence of bias is not considered, and the introduction of bias will cause inaccurate variance component estimation. This paper adopts the bias-corrected variance component estimation method to eliminate the influence of bias introduced by regularization. Simultaneously, the method in this paper, the residual-based bias-corrected variance component estimation and the variance component estimation method are used for simulation experiments. The experiment shows that the variance component estimation method after bias correction can better reverse the slip distribution. The bias-corrected variance component estimation method takes into account the bias introduced by the iterative initial value, and the theory is more rigorous. The Visso earthquake and Norcia earthquake are used for verification, the results show that the bias correction is reasonable and advantageous.
, Available online ,
doi: 10.13203/j.whugis20200712
Abstract:
Objectives: Observation quality is one of main indexes to evaluate the performances of satellite navigation system. Under the background of the global BeiDou navigation system (BDS-3) officially opening global services, the high-precision and high-reliability Positioning, Navigation and Timing (PNT) services are the prerequisites of BeiDou innovations and developments, and global uses. As the main error in the processing of Global Navigation Satellite System (GNSS) observations, researches about rapidly high-precision and mitigated multipath delays are always the focus of the GNSS areas. In consideration of the drawbacks that the traditional multipath modelling strategy, namely first trend then random terms, cannot obtain the global optimal solutions, this research proposes a real-time multipath correction model based on the prior constraints. Methods: Firstly, the regularization algorithm combined with Total Variation term are used to denoise and refine the multipath series in aspect of estimated BeiDou navigation system (BDS) multipath delay. Secondly, the trend and random parts of multipath delays are separately processed and together modeled, in which the strategy of least square plus autoregression (LS+AR) is used to estimate model coefficients in one step. Thirdly, to meet with the requirements of real-time or near real-time BDS applications, the multipath mitigation model is further optimized based on the prior information. Results: According the real-time experiments of BDS Precise Point Positioning (PPP), it is indicated that the accuracy of the regional navigation satellite system (BDS-2) PPP can be improved with 10.6%-64.9%, 0.0%-59.1% and 12.6%-67.2% (B1I/B3I) in the directions of East(E), North(N) and Up(U), respectively. Moreover, the accuracy of stations coordinates using BDS-3 observations can be optimized with at least 13.9%, 60.0%, 45.9% (B1I/B3I) and 19.1%, 46.5%, 23.9% (B1C/B2a) for E, N and U directions, respectively. Conclusions: Therefore, the BDS observation quality can be well improved by the proposed strategy in mitigation multipath delays.
Objectives: Observation quality is one of main indexes to evaluate the performances of satellite navigation system. Under the background of the global BeiDou navigation system (BDS-3) officially opening global services, the high-precision and high-reliability Positioning, Navigation and Timing (PNT) services are the prerequisites of BeiDou innovations and developments, and global uses. As the main error in the processing of Global Navigation Satellite System (GNSS) observations, researches about rapidly high-precision and mitigated multipath delays are always the focus of the GNSS areas. In consideration of the drawbacks that the traditional multipath modelling strategy, namely first trend then random terms, cannot obtain the global optimal solutions, this research proposes a real-time multipath correction model based on the prior constraints. Methods: Firstly, the regularization algorithm combined with Total Variation term are used to denoise and refine the multipath series in aspect of estimated BeiDou navigation system (BDS) multipath delay. Secondly, the trend and random parts of multipath delays are separately processed and together modeled, in which the strategy of least square plus autoregression (LS+AR) is used to estimate model coefficients in one step. Thirdly, to meet with the requirements of real-time or near real-time BDS applications, the multipath mitigation model is further optimized based on the prior information. Results: According the real-time experiments of BDS Precise Point Positioning (PPP), it is indicated that the accuracy of the regional navigation satellite system (BDS-2) PPP can be improved with 10.6%-64.9%, 0.0%-59.1% and 12.6%-67.2% (B1I/B3I) in the directions of East(E), North(N) and Up(U), respectively. Moreover, the accuracy of stations coordinates using BDS-3 observations can be optimized with at least 13.9%, 60.0%, 45.9% (B1I/B3I) and 19.1%, 46.5%, 23.9% (B1C/B2a) for E, N and U directions, respectively. Conclusions: Therefore, the BDS observation quality can be well improved by the proposed strategy in mitigation multipath delays.
, Available online ,
doi: 10.13203/j.whugis20200170
Abstract:
Addressing the problem of high complexity of trajectory sub-segment similarity matching and result sensitivity of trajectory noise, this paper proposes a multi-level trajectory code tree structure that integrates adaptive Hilbert geographic grid coding. A hierarchical organizational form and sub-segment subordinate relationship expression structure are formed from the entire segment of the trajectory to the smallest segment, and a sub-segment similarity matching algorithm is designed on the basis of the trajectory segment code tree to transform complex spatial calculation into string matching operation, which greatly reduces the computational complexity of similar matching of sub-segments. Experiments on actual trajectory data show that the efficiency of the proposed method achieved more than an order of magnitude improvement over the classical distance-based similarity measurement method without affecting accuracy.
Addressing the problem of high complexity of trajectory sub-segment similarity matching and result sensitivity of trajectory noise, this paper proposes a multi-level trajectory code tree structure that integrates adaptive Hilbert geographic grid coding. A hierarchical organizational form and sub-segment subordinate relationship expression structure are formed from the entire segment of the trajectory to the smallest segment, and a sub-segment similarity matching algorithm is designed on the basis of the trajectory segment code tree to transform complex spatial calculation into string matching operation, which greatly reduces the computational complexity of similar matching of sub-segments. Experiments on actual trajectory data show that the efficiency of the proposed method achieved more than an order of magnitude improvement over the classical distance-based similarity measurement method without affecting accuracy.
, Available online ,
doi: 10.13203/j.whugis20210172
Abstract:
Objectives: The middle and lower reaches of Yangtze River, including Hubei, Hunan, Jiangxi, Anhui, Jiangsu, Zhejiang and Shanghai, are important planting bases of commercial grain in China. However, at present, there are relatively few studies on agricultural drought in this regions, and there is a lack of attention to the response of land cover types to drought. Moreover, in the context of climate change, the evolution and trend of agricultural drought in the middle and lower reaches of Yangtze River need further discussion. Methods: This study used MODIS V6 products to construct Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI) to monitor the temporal and spatial evolution of agricultural drought in the middle and lower reaches of Yangtze River from 2001 to 2019, and further explored the drought sensitivity of different vegetative types. Finally, based on the concept of climate change, this study analyzed the drought trends in six provinces and one city (Hubei, Hunan, Anhui, Jiangxi, Jiangsu, Zhejiang, and Shanghai) in the middle and lower reaches of Yangtze River. The results of the above three indices were further evaluated by Standardized Precipitation Index (SPI) on different time scales, obtained and calculated from the CHIRPS V2.0 dataset. Results: The results showed that the VCI and TCI could monitor the long-term abnormal vegetation growth and heat anomalies, respectively, but neither index could provide comprehensive overview of drought conditions. Combining the advantages of both indices with the weights of 0.7 and 0.3 for VCI and TCI, respectively, the VHI, was more effective in agricultural drought monitoring in the middle and lower reaches of Yangtze River. Different vegetation showed different drought sensitivity in study. Crops have the highest sensitivity to drought, forests are the low est, and grasslands are somewhere in between. In the context of climate change, Jiangxi, Hunan, Hubei, Zhejiang, and Anhui show an intense wet trend in the past 20 years (0.0140/year, 0.0139/year, 0.0118/year, 0.0107/year and 0.0098/year, respectively), while the Jiangsu and Shanghai show a weak wet trend (0.0015/year and 0.0009/year, respectively). Conclusions: Drought indices should be integrated to provide comprehensive evaluation of agricultural drought in the middle and lower reaches of Yangtze River. Jiangsu and Shanghai are still at drought risk due to the weak wet trend and the local agricultural department should take drought mitigation measures to prevent economic losses. In the middle and lower reaches of Yangtze River, croplands have the most obvious response to drought, indicating that crops are most sensitive to drought than grasses and forests, more attention should be paid to agriculture management. The relevant results can provide reference for the early warning of drought in various provinces and cities in the middle and lower reaches of Yangtze River and help the management of regional agricultural production.
Objectives: The middle and lower reaches of Yangtze River, including Hubei, Hunan, Jiangxi, Anhui, Jiangsu, Zhejiang and Shanghai, are important planting bases of commercial grain in China. However, at present, there are relatively few studies on agricultural drought in this regions, and there is a lack of attention to the response of land cover types to drought. Moreover, in the context of climate change, the evolution and trend of agricultural drought in the middle and lower reaches of Yangtze River need further discussion. Methods: This study used MODIS V6 products to construct Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI) to monitor the temporal and spatial evolution of agricultural drought in the middle and lower reaches of Yangtze River from 2001 to 2019, and further explored the drought sensitivity of different vegetative types. Finally, based on the concept of climate change, this study analyzed the drought trends in six provinces and one city (Hubei, Hunan, Anhui, Jiangxi, Jiangsu, Zhejiang, and Shanghai) in the middle and lower reaches of Yangtze River. The results of the above three indices were further evaluated by Standardized Precipitation Index (SPI) on different time scales, obtained and calculated from the CHIRPS V2.0 dataset. Results: The results showed that the VCI and TCI could monitor the long-term abnormal vegetation growth and heat anomalies, respectively, but neither index could provide comprehensive overview of drought conditions. Combining the advantages of both indices with the weights of 0.7 and 0.3 for VCI and TCI, respectively, the VHI, was more effective in agricultural drought monitoring in the middle and lower reaches of Yangtze River. Different vegetation showed different drought sensitivity in study. Crops have the highest sensitivity to drought, forests are the low est, and grasslands are somewhere in between. In the context of climate change, Jiangxi, Hunan, Hubei, Zhejiang, and Anhui show an intense wet trend in the past 20 years (0.0140/year, 0.0139/year, 0.0118/year, 0.0107/year and 0.0098/year, respectively), while the Jiangsu and Shanghai show a weak wet trend (0.0015/year and 0.0009/year, respectively). Conclusions: Drought indices should be integrated to provide comprehensive evaluation of agricultural drought in the middle and lower reaches of Yangtze River. Jiangsu and Shanghai are still at drought risk due to the weak wet trend and the local agricultural department should take drought mitigation measures to prevent economic losses. In the middle and lower reaches of Yangtze River, croplands have the most obvious response to drought, indicating that crops are most sensitive to drought than grasses and forests, more attention should be paid to agriculture management. The relevant results can provide reference for the early warning of drought in various provinces and cities in the middle and lower reaches of Yangtze River and help the management of regional agricultural production.
, Available online ,
doi: 10.13203/j.whugis20210224
Abstract:
The access, integrated management, and service of sensing devices is the primary prerequisite for realizing smart cities. To systematically discuss the current development of smart city sensing technology, this context analyzes the requirement and complexity of smart city sensing. And then, it compares the development process of smart city sensing technology system, and deeply compares and analyzes the main technology systems and network architectures used in current smart city sensing. On this basis, the challenge of accessing tens of thousands of various sensors in the future smart city construction is pointed out. The future solution of smart city sensing base station is proposed. This context discusses the critical role of smart city-aware base stations as an integrated sensing infrastructure for future smart cities, and introduces the operation principle of the whole system in terms of its prototype. Finally, around the smart city sensing base station, this paper analyzes and foresees its application capabilities in community scenarios and emergency scenarios.
The access, integrated management, and service of sensing devices is the primary prerequisite for realizing smart cities. To systematically discuss the current development of smart city sensing technology, this context analyzes the requirement and complexity of smart city sensing. And then, it compares the development process of smart city sensing technology system, and deeply compares and analyzes the main technology systems and network architectures used in current smart city sensing. On this basis, the challenge of accessing tens of thousands of various sensors in the future smart city construction is pointed out. The future solution of smart city sensing base station is proposed. This context discusses the critical role of smart city-aware base stations as an integrated sensing infrastructure for future smart cities, and introduces the operation principle of the whole system in terms of its prototype. Finally, around the smart city sensing base station, this paper analyzes and foresees its application capabilities in community scenarios and emergency scenarios.
, Available online ,
doi: 10.13203/j.whugis20200609
Abstract:
Individual location prediction is of great significance in applications such as precise prevention and control of infectious diseases and scientific planning of public facilities. Existing location prediction algorithms mainly focus on mining and modeling individual longitudinal historical trajectory characteristics, and realize location prediction, and less consider the regular characteristics of users with horizontal similarity. For this reason, based on the framework of Graph Convolution Network (GCN) and Long Short-Term Memory (LSTM) model, this paper proposes an individual location prediction algorithm that takes into account the characteristics of horizontally similar user trajectories and the characteristics of vertical historical regularity. First, construct a user trajectory similarity algorithm and screen users with high similarity, then use the graph convolution model to extract user trajectory features with high similarity of users to be predicted, and finally use the long and short-term memory model framework to extract historical trajectory features and integrate similar user trajectory features, So as to achieve individual location prediction. Based on the data of more than 80,000 users in a city for 4 consecutive working days, the results show that the accuracy of the method proposed in this paper decreases with the increase of the prediction time step, and the accuracy of night prediction is significantly higher than that of the day, but compared to the previous All models have an improvement of more than 10%. When 15 minutes is used as the prediction time step, the model accuracy rate reaches 80.45%.
Individual location prediction is of great significance in applications such as precise prevention and control of infectious diseases and scientific planning of public facilities. Existing location prediction algorithms mainly focus on mining and modeling individual longitudinal historical trajectory characteristics, and realize location prediction, and less consider the regular characteristics of users with horizontal similarity. For this reason, based on the framework of Graph Convolution Network (GCN) and Long Short-Term Memory (LSTM) model, this paper proposes an individual location prediction algorithm that takes into account the characteristics of horizontally similar user trajectories and the characteristics of vertical historical regularity. First, construct a user trajectory similarity algorithm and screen users with high similarity, then use the graph convolution model to extract user trajectory features with high similarity of users to be predicted, and finally use the long and short-term memory model framework to extract historical trajectory features and integrate similar user trajectory features, So as to achieve individual location prediction. Based on the data of more than 80,000 users in a city for 4 consecutive working days, the results show that the accuracy of the method proposed in this paper decreases with the increase of the prediction time step, and the accuracy of night prediction is significantly higher than that of the day, but compared to the previous All models have an improvement of more than 10%. When 15 minutes is used as the prediction time step, the model accuracy rate reaches 80.45%.
, Available online ,
doi: 10.13203/j.whugis20200229
Abstract:
Focusing on the problem of global absolute positioning of drones under global navigation satellite system (GNSS) denied environment, a reference satellite image retrieval method that aggregates deep learning features is proposed. First, the pre-trained deep learning model is used to extract local convolution features of the drone images and satellite images. Then, the local aggregation descriptor vector is used to generate the global expression of the images. Finally, the global feature of the image is used to perform similarity retrieval and the post-processing method of matching precisely and reranking the retrieval results is used, which further improves the retrieval accuracy. A new satellite reference image data set for absolute positioning of drone is designed and tested. The results show that the accuracy of the method used to retrieve the satellite reference image in the drone image adaptation area is 76.07%, which can provide a reference for the subsequent absolute positioning of vision-based drones.
Focusing on the problem of global absolute positioning of drones under global navigation satellite system (GNSS) denied environment, a reference satellite image retrieval method that aggregates deep learning features is proposed. First, the pre-trained deep learning model is used to extract local convolution features of the drone images and satellite images. Then, the local aggregation descriptor vector is used to generate the global expression of the images. Finally, the global feature of the image is used to perform similarity retrieval and the post-processing method of matching precisely and reranking the retrieval results is used, which further improves the retrieval accuracy. A new satellite reference image data set for absolute positioning of drone is designed and tested. The results show that the accuracy of the method used to retrieve the satellite reference image in the drone image adaptation area is 76.07%, which can provide a reference for the subsequent absolute positioning of vision-based drones.
, Available online ,
doi: 10.13203/j.whugis20200379
Abstract:
Existing population spatialization methods mainly use administrative-unit-level data to train regression model, and transfer it to grid cell-level to achieve population allocation. However, the significant scale difference between the analytical units in training and estimation leads to the issues of cross-scale model transfer. Meanwhile, only the attributes of current cell are considered in cell-level feature modeling, which causes the innate spatial association between cells to be eliminated and cells to be isolated. Therefore, this paper proposes a novel population spatialization based on random forest by considering pixel-level attribute grading and spatial association (PAG-SA). In the cell-level feature modeling, we firstly constructs the night light grading features embedded with building category constraints based on natural breaks, and counts the grid proportion of each grading level at the administrative-unit-level as the training input to reduce the cross scale error; secondly, the influence and distance attenuation of neighborhood point of interests (POIs) upon the current cell is modelled by using kernel density estimation; thirdly, based on overlay analysis, the numbers of POIs in the contours of different building types are counted to improve the precision of feature modeling. To verify the effectiveness of the proposed method, we selected Wuhan city as the experimental area and compared its spatialization accuracy with the datasets of WorldPop, GPW and PopulationGrid_China at street scale. The results show that the mean absolute error of PAG-SA is only 1/6-1/3 of the comparison datasets. In addition, the influence of feature composition, grid size and kernel density bandwidth on the accuracy is also discussed.
Existing population spatialization methods mainly use administrative-unit-level data to train regression model, and transfer it to grid cell-level to achieve population allocation. However, the significant scale difference between the analytical units in training and estimation leads to the issues of cross-scale model transfer. Meanwhile, only the attributes of current cell are considered in cell-level feature modeling, which causes the innate spatial association between cells to be eliminated and cells to be isolated. Therefore, this paper proposes a novel population spatialization based on random forest by considering pixel-level attribute grading and spatial association (PAG-SA). In the cell-level feature modeling, we firstly constructs the night light grading features embedded with building category constraints based on natural breaks, and counts the grid proportion of each grading level at the administrative-unit-level as the training input to reduce the cross scale error; secondly, the influence and distance attenuation of neighborhood point of interests (POIs) upon the current cell is modelled by using kernel density estimation; thirdly, based on overlay analysis, the numbers of POIs in the contours of different building types are counted to improve the precision of feature modeling. To verify the effectiveness of the proposed method, we selected Wuhan city as the experimental area and compared its spatialization accuracy with the datasets of WorldPop, GPW and PopulationGrid_China at street scale. The results show that the mean absolute error of PAG-SA is only 1/6-1/3 of the comparison datasets. In addition, the influence of feature composition, grid size and kernel density bandwidth on the accuracy is also discussed.
, Available online ,
doi: 10.13203/j.whugis20210008
Abstract:
Since there is only nadir radiometer onboard for the wide-swath altimetry, the wet tropospheric delay can only be corrected by models inside the cross-swath or substituted by the nadir radiometer measured data, which lead to the lower accuracy. In order to improve the accuracy of wet tropospheric correction (WTC) inside the cross-swath, an optimum interpolation method to fuse the nadir radiometer WTC is proposed, and it is verified by taken the SWOT (Surface Water and Ocean Topography) wide-swath altimeter as example. Inside the cross-swath, when the WTCs from ERA5 (ECMWF Re-Analysis 5th Generation) dataset are used, the residual of WTC after corrected by optimum interpolation can be reduced by 40% compared with the nadir radiometer WTC substituted. When the simulated WTCs from the spectrum of radiometer measured WTCs are used, the residual of WTC after corrected by optimum interpolation can be reduced by 80% in all latitude areas compared with the nadir radiometer WTC substituted. In addition, the optimum interpolation method has much better accuracy than the nadir radiometer WTC substituted in the case of high water vapor variability.
Since there is only nadir radiometer onboard for the wide-swath altimetry, the wet tropospheric delay can only be corrected by models inside the cross-swath or substituted by the nadir radiometer measured data, which lead to the lower accuracy. In order to improve the accuracy of wet tropospheric correction (WTC) inside the cross-swath, an optimum interpolation method to fuse the nadir radiometer WTC is proposed, and it is verified by taken the SWOT (Surface Water and Ocean Topography) wide-swath altimeter as example. Inside the cross-swath, when the WTCs from ERA5 (ECMWF Re-Analysis 5th Generation) dataset are used, the residual of WTC after corrected by optimum interpolation can be reduced by 40% compared with the nadir radiometer WTC substituted. When the simulated WTCs from the spectrum of radiometer measured WTCs are used, the residual of WTC after corrected by optimum interpolation can be reduced by 80% in all latitude areas compared with the nadir radiometer WTC substituted. In addition, the optimum interpolation method has much better accuracy than the nadir radiometer WTC substituted in the case of high water vapor variability.
, Available online ,
doi: 10.13203/j.whugis20190425
Abstract:
With the development of remote sensing technology, the spatial resolution of remote sensing image keeps improving, which brings both opportunities and challenges for the traditional remote sensing image classification and change detection. In order to improve the reliability of change detection of high resolution remote sensing image, this paper proposes a method of change detection of remote sensing image based on fuzzy comprehensive evaluation. Then, for objects in certain scale, a fuzzy comprehensive evaluation model was established to calculate the membership of the pixels in each object, which takes the spectral and texture characteristics of two-phase remote sensing image objects into comprehensive consideration. Finally, we use the entropy method to fuse the fuzzy evaluation membership degree of each pixel in different scales based on information entropy. Taking two groups of high-resolution images with different phases as an example, the experiment realized the effective fusion of multi-scale change detection based on fuzzy logic, which made full use of multi-level pixel features and consequently improved the overall effect of single scale object-oriented change detection. It can be inferred that using fuzzy logic in change detection can play a greater role in multi-scale change detection, which provides a new idea for the exploration of multi-scale change detection.
With the development of remote sensing technology, the spatial resolution of remote sensing image keeps improving, which brings both opportunities and challenges for the traditional remote sensing image classification and change detection. In order to improve the reliability of change detection of high resolution remote sensing image, this paper proposes a method of change detection of remote sensing image based on fuzzy comprehensive evaluation. Then, for objects in certain scale, a fuzzy comprehensive evaluation model was established to calculate the membership of the pixels in each object, which takes the spectral and texture characteristics of two-phase remote sensing image objects into comprehensive consideration. Finally, we use the entropy method to fuse the fuzzy evaluation membership degree of each pixel in different scales based on information entropy. Taking two groups of high-resolution images with different phases as an example, the experiment realized the effective fusion of multi-scale change detection based on fuzzy logic, which made full use of multi-level pixel features and consequently improved the overall effect of single scale object-oriented change detection. It can be inferred that using fuzzy logic in change detection can play a greater role in multi-scale change detection, which provides a new idea for the exploration of multi-scale change detection.
, Available online ,
doi: 10.13203/j.whugis20200217
Abstract:
This paper combines the precision estimation characteristics of Monte Carlo method (MC), and proposes a comprehensive evaluation formula that uses bias and mean square error as evaluation indicators, and uses this formula for simulated annealing algorithm (SA), genetic algorithm (GA) and neural network algorithm (NNA) in the precision estimation of volcanic compound dislocation model (CDM) and earthquake Okada model inversion. The experimental results show that whether it is based on the CDM model or the Okada model, the parameter estimation gap between the above three methods is small, but the precision of the different methods varies greatly. The MC is used to calculate the precision of SA, GA and NNA. In this paper, it is found that in the volcano CDM, mean square errors in some parameters calculated by SA and GA are relatively large, and the comprehensive evaluation values of SA and GA 11.3988 and 3.9820 are larger than the comprehensive evaluation value of NNA of 3.6131. In the Okada model, the SA has a higher mean square error than GA and NNA, and in the Lushan earthquake, the comprehensive evaluation value of the SA is 11.6562, which is much larger than the comprehensive evaluation of the GA and NNA 4.0604 and 3.6254. It can be seen from the experiment that under the method of this paper, the precision information of the NNA is higher, and the result is more convincing, followed by GA, and the SA has lower precision.
This paper combines the precision estimation characteristics of Monte Carlo method (MC), and proposes a comprehensive evaluation formula that uses bias and mean square error as evaluation indicators, and uses this formula for simulated annealing algorithm (SA), genetic algorithm (GA) and neural network algorithm (NNA) in the precision estimation of volcanic compound dislocation model (CDM) and earthquake Okada model inversion. The experimental results show that whether it is based on the CDM model or the Okada model, the parameter estimation gap between the above three methods is small, but the precision of the different methods varies greatly. The MC is used to calculate the precision of SA, GA and NNA. In this paper, it is found that in the volcano CDM, mean square errors in some parameters calculated by SA and GA are relatively large, and the comprehensive evaluation values of SA and GA 11.3988 and 3.9820 are larger than the comprehensive evaluation value of NNA of 3.6131. In the Okada model, the SA has a higher mean square error than GA and NNA, and in the Lushan earthquake, the comprehensive evaluation value of the SA is 11.6562, which is much larger than the comprehensive evaluation of the GA and NNA 4.0604 and 3.6254. It can be seen from the experiment that under the method of this paper, the precision information of the NNA is higher, and the result is more convincing, followed by GA, and the SA has lower precision.
, Available online ,
doi: 10.13203/j.whugis20190300
Abstract:
This paper proposes a link-graph-based method for recognizing grid pattern in road networks. This method focuses on the road segments connection, by using the link graph to represent road networks, in which nodes represent road segments and links represent road segments intersections. It converts the recognition grid in the road networks into searching for grid loops in the link graph. Five parameters characterizing road link pairs are defined to select nodes and links that would match the grid conditions in the graph. The breadth-first search algorithm is used to search loop in link graph under designed constraints. The result shows that the proposed method can effectively recognize the grid pattern in the road networks.
This paper proposes a link-graph-based method for recognizing grid pattern in road networks. This method focuses on the road segments connection, by using the link graph to represent road networks, in which nodes represent road segments and links represent road segments intersections. It converts the recognition grid in the road networks into searching for grid loops in the link graph. Five parameters characterizing road link pairs are defined to select nodes and links that would match the grid conditions in the graph. The breadth-first search algorithm is used to search loop in link graph under designed constraints. The result shows that the proposed method can effectively recognize the grid pattern in the road networks.
, Available online ,
doi: 10.13203/j.whugis20200179
Abstract:
The consistency of spatial distribution pattern before and after generalization is an important factor to evaluate the quality of land use data. Considering the lack of quantitative analysis and visualization in spatial distribution model evaluation of data, this paper puts forward a new evaluation method based on the unique autocorrelation of spatial data. Firstly, we establish the spatial weight matrix by using the semantic distance of data and calculate Global and local autocorrelation of data before and after processing by Moran'i index, then use Moran's scatterplot and LISA (local indicators of spatial association) aggregation map to visualize the quality of spatial distribution patterns. Compared with the traditional data quality evaluation method, this method can evaluate the consistency of spatial distribution pattern of global map better by taking into account the semantic relation of the data, obtaining the quantifiable aggregation degree and contrasting by the visual method. In cognitive experiments, we take the land type data been scale transformed which cause data quality problems mostly as an example. The experimental results accord with human cognitions and the experimental method is practical.
The consistency of spatial distribution pattern before and after generalization is an important factor to evaluate the quality of land use data. Considering the lack of quantitative analysis and visualization in spatial distribution model evaluation of data, this paper puts forward a new evaluation method based on the unique autocorrelation of spatial data. Firstly, we establish the spatial weight matrix by using the semantic distance of data and calculate Global and local autocorrelation of data before and after processing by Moran'i index, then use Moran's scatterplot and LISA (local indicators of spatial association) aggregation map to visualize the quality of spatial distribution patterns. Compared with the traditional data quality evaluation method, this method can evaluate the consistency of spatial distribution pattern of global map better by taking into account the semantic relation of the data, obtaining the quantifiable aggregation degree and contrasting by the visual method. In cognitive experiments, we take the land type data been scale transformed which cause data quality problems mostly as an example. The experimental results accord with human cognitions and the experimental method is practical.
Articles just accepted have been peer-reviewed and accepted, which are not yet assigned to volumes /issues, but are citable by Digital Object Identifier (DOI).
Display Method:
, Available online ,
doi: 10.13203/j.whugis20200098
Abstract:
Unmanned Aerial Vehicle; Antarctic Expedition; Climate Change; Photogrammetry; Glaciology; Ecology; Geomorphology
Unmanned Aerial Vehicle; Antarctic Expedition; Climate Change; Photogrammetry; Glaciology; Ecology; Geomorphology
Author Login
Review Login
Editor Login
Reader Login
Editorial Login
Editor-in-Chief Login
Start in 1957 Monthly
ISSN 1671-8860
CN 42-1676/TN
中国中文核心期刊
中国科技核心期刊
Ei核心期刊
Special Recommend
- All visit:
- Today's visit:
