Current Articles
2022, Volume 42, Issue 11
Display Method:
2022,
47(11):
1805-1814.
doi: 10.13203/j.whugis20220606
Abstract:
With the rapid development of technologies in sensor, computer and robotics, the trends of multi-sensor integration and intelligent applications are shown in surveying field. Engineering surveying has developed in the direction of automation, dynamism and intelligence, and is widely used for high-precision measurement of large bridges, water conservancy hubs, high-speed rail subways, highways and other projects, as well as precision industrial measurement in aerospace, aviation, intelligent manufacturing and other fields. The expansion of application fields also puts forward new requirements for surveying tasks.The surveying data processing is no longer limited to the traditional pure geometric parameter estimation, but gradually expands to the generalized measurement data processing with both geometric parameters and feature information. We review the development process from classical measurement data processing to generalized measurement data processing, summarize the processing and analysis logic of multiple types of measurement data, and present the challenges of measurement data processing in the era of big data. The basic ideas and strategies of generalized measurement data processing are elaborated and illustrated with several typical cases.
With the rapid development of technologies in sensor, computer and robotics, the trends of multi-sensor integration and intelligent applications are shown in surveying field. Engineering surveying has developed in the direction of automation, dynamism and intelligence, and is widely used for high-precision measurement of large bridges, water conservancy hubs, high-speed rail subways, highways and other projects, as well as precision industrial measurement in aerospace, aviation, intelligent manufacturing and other fields. The expansion of application fields also puts forward new requirements for surveying tasks.The surveying data processing is no longer limited to the traditional pure geometric parameter estimation, but gradually expands to the generalized measurement data processing with both geometric parameters and feature information. We review the development process from classical measurement data processing to generalized measurement data processing, summarize the processing and analysis logic of multiple types of measurement data, and present the challenges of measurement data processing in the era of big data. The basic ideas and strategies of generalized measurement data processing are elaborated and illustrated with several typical cases.
2022,
47(11):
1815-1822.
doi: 10.13203/j.whugis20200318
Abstract:
Objectives The prediction of lunar physical liberation is very important for precise positioning of lander and rover. The coordinates and physical libration of Chang'E-3 lander are calculated based on the very long baseline interferometry(VLBI) single point positioning model. Meanwhile, the parameters of the lunar libration are obtained in the 7-day arc. Methods Based on the recursive neural network(RNN), a long-short term memory(LSTM) neural network is used to establish a prediction model of the lunar libration parameters. Using the coordinates of the lunar lander and the VLBI observation as input, selecting three Euler angles as output, 2 424 samples are used for training, 519 samples are used for testing, and 519 samples are used for verification. Results The results show that the data of the verification set has a gradient of about 1 000 training and 9 iterations, and the maximum absolute error after training is only 6.2×10-5(″)/s. The experimental results show that the three Euler angles Ω、i、μ of LSTM are accurate to 97.8%, 99.7%, 97.2%, and the three Euler angles of RNN networks are accurate to 95.2%, 98.5%, 95.8%. Conclusions It is proved that the LSTM network has higher prediction accuracy than the RNN network. Compared with the predicted results of JPL DE421 ephemeris, it is proved that the LSTM network has the higher prediction accuracy.
2022,
47(11):
1823-1831.
doi: 10.13203/j.whugis20200412
Abstract:
Objectives Precipitable water vapor (PWV) information with high precision and high spatial-temporal resolution plays an important role in the study of extreme weather. The PWV obtained by traditional single water vapor detection technology has the defects of poor precision and low spatial-temporal resolution due to the limitations of its system design. Methods To solve this problem, we propose a PWV hybrid model based on multi-source data, called the GSP(GPT2w+spherical harmonical function+polynomial fitting) model.In this model, the initial value of PWV is calculated by the GPT2w model, the residual sequence of PWV is fitted by a spherical harmonic function, and after that, deviation correction is performed for the residual PWV based on the polynomial fitting, and the Bartlett test is introduced to deter mine the optimal weights of multi-source data in the GSP model. Results The data of 26 GNSS stations and 37 ERA-Interim grid points (1°×1°) in Yunnan Province, China has been selected to validate the GSP model, and the numerical results show that the accuracy improvement rate of the GSP model is 15%— 18% compared with the traditional polynomial fitting model. Compared with the ERA5 (0.25°×0.25°) data, the mean root mean square and Bias of GSP model are 1.64 mm and -0.25 mm, respectively. Conclusions The above results show that the proposed GSP model has high accuracy and plays an important role in extreme weather warnings.
2022,
47(11):
1832-1839.
doi: 10.13203/j.whugis20200475
Abstract:
Objectives In order to analyze the independent physical contribution of the post-seismic relaxation term on global positioning system (GPS) observing displacements, the regional post-seismic relaxation time and the characteristics of its spatial distribution are studied. Methods The logarithmic function is used to describe the post-seismic relaxation term, and the logarithmic relaxation time is estimated from post-seismic GPS coordinate time series by using nonlinear weighted least squares method. Steps are as follows: Remove the gross errors; fix the step items; remove the constant term and the steady-state velocity term; the logarithmic relaxation terms are modeled for the preprocessed GPS coordinate time series. Results Taking the 2009—2019 continuous coordinate time series of the global navigation satellite systems earth observation network stations in Japan as an example, the logarithmic relaxation time after 2011 Tohoku-Oki Mw 9.0 earthquake is estimated. It is found that the post-seismic relaxation time is significantly related to the epicenter distance and obeys the Gaussian distribution. Hence, a Gaussian function plus constant model is constructed to reflect the law of post-seismic relaxation time and epicentral distance. The peak value, peak position, half-width information, and minimum position of the Gaussian distribution curve of relaxation time after the earthquake are 3.5 a, 0 km, 262 km, and 0.5 a, respectively.The relaxation range with relaxation time more than 0.5 a is mainly concentrated in the region of about 524 km from the epicenter. The regional distribution of post-seismic relaxation effect is significantly different. The longer the relaxation time, the larger the average horizontal displacement of the relaxation term in the corresponding region, and there are two central regions with an average relaxation time of 2.5 years, which were consistent with the central region and time of the afterslip. Conclusions The proposed algorithm on estimating the post-seismic logarithmic relaxation time can help to analyze the independent physical contribution of post-seismic relaxation term, the afterslip and viscoelastic relaxation.
2022,
47(11):
1840-1846.
doi: 10.13203/j.whugis20200232
Abstract:
Objectives In order to find a high accuracy method for satellite clock bias prediction, a preprocessing strategy for wavelet threshold method based on the median absolute deviation(MAD) is proposed to preprocess the small magnitude error of satellite clock bias data. Methods 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. Results The experimental results show that after modeling the clock bias data processed by the proposed method, the prediction accuracy of wavelet neural network(WNN) model is improved by about 14.1% and the prediction stability is improved by about 19.7%. Conclusions This method can effectively eliminate the small error in the historical observation sequence of clock bias, improve the quality of clock bias data and the effect of model clock bias prediction.
2022,
47(11):
1847-1853.
doi: 10.13203/j.whugis20200404
Abstract:
Objectives How to search intersections fast is the premise requirements of the quality evaluation of air-sea gravimetry data. With the continuous expansion of the scale of the air-sea gravity survey network and the emergence of irregular survey lines, the existing intersections search methods are hard to ensure that all the intersections can be quickly and accurately searched.Therefore, how to improve the searching efficiency and accuracy, has attracted much attention. Methods We introduce the quad tree decomposition method to search the intersections of survey network, conduct the automatic refined subdivision near the intersections and then use the traversal comparison size to extract the overlapping area of the envelope rectangle of the main and auxiliary survey lines. By using rapid rejection judgment and repeated iterative search, the magnitude of indexes' number retained is equivalent to the actual intersections, which effectively avoids the large amount of redundant calculation. The method was programed and verified. Results One experiment shows that the method can complete the search in only 0.28 s for the block EN01 airborne gravity data from gravity for the redefinition of the American vertical datum (GRAV-D) project, which has about 100 000 survey points; and another one shows that the search efficiency of the method for a survey network of 4 877 988 survey points is also significantly better than the existing methods and mature commercial software. Conclusions This method uses traversal search to ensure 100% accuracy, and has high searching efficiency. It is universally applicable to the intersections' search of air-sea gravity network, and has excellent practical application effect.
2022,
47(11):
1854-1859.
doi: 10.13203/j.whugis20200149
Abstract:
Objectives The vertical movement is obtained and analyzed with the different geodetic data of more than 40 years in Shaanxi area, China. Methods Firstly, the adjustment result of the national first order level network in three periods is collected, the trend of 40 years is applied to describe the vertical movement as a whole which can show the uplift and downward of the crust, the data of the second order level network on the basis of the first order level points is adjusted, and the information with the higher resolution is obtained. Secondly, the other vertical information is collected from the result which includes global navigation satellite system (GNSS) network, crustal movement observation network of China and interferometric synthetic aperture radar (InSAR) result of the geography condition monitoring in Shaanxi area. Finally, the fusion result is computed with move fitting method. Results Experimental result shows that the vertical movement of GNSS is consistent with the vertical movement of level on the whole.The uplift rate on the whole is about 4 mm/a in the north of Shaanxi area the maximum downward rate is 6 mm/a in Guanzhong plain, the uplift rate is about 3 mm/a in the south of Shaanxi area, and there is severe subsidence in the north of Shaanxi area and local area of Xi'an. Conclusions From 1970 to 2018, a downward trend of vertical movement was shown in Guanzhong plain, and the uplift trend of vertical movement was displayed in mountains and plateaus around Guanzhong plain.
2022,
47(11):
1860-1869.
doi: 10.13203/j.whugis20200424
Abstract:
Objectives The code-phase combination method is often used to detect cycle slips in kinematic single-frequency data, but its detection effect is greatly affected by pseudorange noise. Therefore it is necessary to combine other observations to reduce the effect of pseudorange noise for improving its detection capability. Methods We propose a Doppler integration aided kinematic single-frequency cycle slip detection method, which first uses Doppler observations to smooth the pseudorange observations for reducing the noise of the pseudorange, and then gets epoch-difference values. In this way, we can significantly improve the capability of kinematic single-frequency cycle slip detection. The experiments use real-time dynamic data different sampling rates, altitude angle variations, continuous cycle slips and coarse differences, respectively. The simulated cycle slips of the different cycle are added at different epochs, and the continuous cycle slips are added at adjacent epochs. Results The results show that the proposed method compared with the code-phase combination method and the code-phase combination method based on polynomial fitting can detect all the simulated cycle slips in real time. Conclusions The method of Doppler integration aided kinematic single-frequency cycle slip detection can significantly improve the capability of the single-frequency cycle slips detection.
2022,
47(11):
1870-1877.
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 propose one method of reducing the training samples as less as possible based on the representativeness or typicalness of training samples. Methods 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. And the 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.
2022,
47(11):
1878-1886.
doi: 10.13203/j.whugis20200278
Abstract:
Objectives The B2a is a new signal of BeiDou-3(BD-3) with high-bandwidth and high pseudorange measurement precision, therefore, it is suitable for GNSS-R water surface altimetry. There were few studying based on the BD-3 reflected signal, due to the BD-3 system just began to provide worldwide services in the past two years. Methods The National Space Science Center (NSSC) has developed a GNSS-R receiver with independent intellectual property supporting the BD-3 B2a capture and tracking functions, which can obtain and track both direct and reflected B2a signals. B2a signals are difficult to capture due to its long PRN code. One B2a assistant approach is proposed and implemented that the shorter B1I signals are first obtained and tracked, then the B2a signals can be correctly captured using B1I auxiliary information with shorter acquisition time. One cross calibration system is included by the receiver, which can effectively eliminate the system biases caused by the delays of different cables and receiver channels. Results During the shore-based experiment carried out at Huairou in Beijing in 2019, the correlation waveforms of the BD-3 B2a, BD-2 B1I and GPS L1 C/A reflected signals were accumulated. The water surface height was successfully retrieved and the systematic biases was eliminated effectively. The water surface height results show that the inversion precision based on BD-3 B2a reaches 5.9 cm with the non-coherent integration time of 30 s. Conclusions The height precision has been improved by 13 cm than BD-2 B1I signal and 20 cm than GPS L1 C/A signal. This verification experiment expands the application research of BD-3 system in GNSS-R water surface altimetry and lays the foundation for the development of subsequent tasks.
2022,
47(11):
1887-1895.
doi: 10.13203/j.whugis20200213
Abstract:
Objectives Aiming at the problem of inconsistent data distribution between cross-modal remote sensing information caused by "heterogeneity gap", a new cross-modal remote sensing dataset is constructed and released for public. Methods To solve the problem of "heterogeneity gap", a general cross-modal correlation learning method (CCLM) is proposed for remote sensing. Based on the latent semantic consistency between different modality information, CCLM includes two stages: The learning of feature representation and the construction of common feature space. Firstly, deep neural networks are adopted to extract the feature representation of image and sequence information. To construct a common feature space, a new loss function is designed for correlation learning, by exploring the semantic consistency within intra-modality and complementary information contained in inter-modality. Secondly, knowledge distillation is used to enhance the semantic relevance to achieve the semantic consistency of common space. Results The experiments are performed on our dataset. The experimental results show that the mean average precision (mAP) of our CCLM on cross-modal retrieval tasks exceeds 70%. Conclusions The results outperform other baseline methods, and verify effectiveness of the proposed dataset and method.
2022,
47(11):
1896-1905.
doi: 10.13203/j.whugis20200289
Abstract:
Objectives The coseismic deformation field of 2017 Jiuzhaigou earthquake has been investigated by previous numerous studies. However, these studies most focus on the one-dimensional(1D) ground motion along the radar line of sight(LOS), thus resulting in anamorphic coseismic displacements. Methods We construct high-quality three-dimensional(3D) coseismic deformation field using ascending and descending synthetic aperture radar(SAR) data from Sentinel-1A satellite with constraint of the elastic dislocation model to reflect the real movement patterns of 2017 Jiuzhaigou earthquake. Firstly, we use differential interferometry method to generate LOS coseismic deformation field with SAR images. Then, a two-step inversion algorithm is used to subdivide the fault plane, and obtain the geometric parameters and the optimal fault slip distribution. With constraint of this fault model, we take the variance component estimation approach to reconstruct 3D coseismic deformation field based on interferometric synthetic aperture radar(InSAR) measurements. Results 3D coseismic deformation results show that the coseismic deformation field is dominated by horizontal displacement. In the north-south(N) direction, the maximum displacement of the hanging wall is －19.81 cm and 14.38 cm in the footwall. In the east-west(E) direction, the maximum displacement of the hanging wall is 18.37 cm for the northwestern, while that of the footwall is －7.84 cm for the Southeastern. In the vertical(U) direction, there is a little uplift in the northern of the fault and the maximum is about 3 cm. Conclusions The derived north-south and east-west displacements are in good consistency of the global navigation satellite system investigations, indicating that combing InSAR measurements and elastic dislocation model to reconstruction 3D seismic deformation field is feasible and effective, which overcomes the problem of insufficient geodetic data and offers extensive future usage for measuring earthquake deformation.
2022,
47(11):
1906-1916.
doi: 10.13203/j.whugis20220024
Abstract:
Objectives Single tree canopy extraction is of great significance for fruit tree health status, nutritional composition, and yield prediction.As a low-cost, low-risk data source, high-resolution remote sensing images obtained by drones provide new technical means for accurately estimating tree numbers and delineating tree canopy profiles. Using unmaned aerial vehicle(UAV) digital images as the data source, a single-tree segmentation algorithm based on regional seed blocks is proposed to solve the single-tree segmentation problem of densely planted fruit trees. Methods The canopy of the fruit tree is extracted by the maximum likelihood method to generate digital surface model (DSM)of the canopy, and Gaussian filter is combined with morphological opening and self-adapted threshold segmentation generates regional seed blocks as the basis for tree statistics and as the marker of the marked-controlled watered segmentation. Results The results show that the overall tree recall rate is 95.22%, the precision rate is 99.09%. The overall accuracy rate of single tree contour extraction is 93.45%, the overall omission error is 5.87%, and the overall commission error is 0.90%. Compared with the previous local maximum seed point extraction results, the overall accuracy is 18.66% higher, and the precision of the fine crown contour extraction is 17.75% higher. Conclusions The watershed method based on the regional seed block can effectively prevent the overlapping area of the canopy from being repeatedly divided into multiple fruit trees. On the basis of preserving the canopy outline of the fruit tree to the greatest extent, the segmentation error of the fruit tree is reduced.It provides a reference for the method of extracting the crown of a single fruit tree in a densely planted orchard in flat terrain.
2022,
47(11):
1917-1926.
doi: 10.13203/j.whugis20200297
Abstract:
Objectives The albedo of Greenland, the second largest ice sheet in the world, is the key to study the change of energy budget in the northern Hemisphere.Global land surface satellite products system (GLASS) is the global albedo product with the longest time series (1981—2017) in the world. Methods The accuracy of surface albedo of GLASS in Greenland is evaluated using Greenland climate network (GC-Net) and programme for monitoring of the Greenland ice sheet (PROMICE) ground observation data. Based on the surface albedo product of GLASS from 2000 to 2017, the annual variation trend and spatial distribution characteristics of albedo in Greenland in July are analyzed. Results The results show that the root mean square error (RMSE) between GLASS and GC-Net albedo is 0.077 8 (R2=0.490 7), and that between GLASS and PROMICE albedo is 0.078 6 (R2=0.899 9). The results of GLASS albedo change in Greenland in July from 2000 to 2017 show that the albedo of Greenland was decreasing during this period, with an average rate of about 0.000 6/a, and the decreased area accounts for about 64% of the total area of Greenland. Among them, the area between 750 m and 1 500 m above sea level in western Greenland is the most sensitive to climate change and has the highest rate of albedo reduction, reaching 0.026/a. Conclusions The albedo value of GLASS products is underestimated to some extent but it meets the needs of ice and snow albedo research in Greenland. Also, the albedo of Greenland shows a slow interannual decline in July, especially in the western region.
2022,
47(11):
1927-1937.
doi: 10.13203/j.whugis20200382
Abstract:
Objectives 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. Methods Based on multisource data, we propose to construct human activity indicators (residential area walkability index, street vitality index, urban function mixing index) using point of interest(POI), OpenStreetMap (OSM) and residential area data, and urban ecological environment indicator (remote sensing ecological index)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 dataset, the relationship between the urban ecological environment and human activities is revealed. Results We demonstrate the application of our method using a case study of Nanchang city. The results show that: (1) The 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. (2) From the analysis of the performance results of each model on the dataset, XGB has the best regression effect, followed by PLR.(3)There 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. (4) In 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. Conclusions Using multisource data and machine learning models can provide an important reference for exploring the relationship between urban ecological environment and human activities.
2022,
47(11):
1938-1945.
doi: 10.13203/j.whugis20200315
Abstract:
Objectives The accurate and fast multi-scale representation of digital elevation model (DEM) is the key basis for its multi-scale application. We aim to propose an automated and high-quality multi-scale representation method for DEM. Methods We construct a model of DEM data for multi-scale representation, according to the corresponding relationship of "low-frequency - high-energy - large scale" in energy spectral density, and relate topographic semantic features in simplification. Results The result shows that this model can dynamically derive DEM data at different scales. The figure of contour lines derived from this model can meet the basic principle of "retaining main topographic features and abandoning secondary topographic features"in topographic expression, spatial cognition and cartographic generalization. Quantificationally compared with traditional DEM simplification methods in elevation and slope shape, this method shows similar or better effect in statistical and structural significance. Conclusions The model based on the Fourier energy spectrum can effectively express DEM data with different scale requirements.
2022,
47(11):
1946-1955.
doi: 10.13203/j.whugis20200219
Abstract:
Objectives Security of vector spatial data is of importance in the community of geographic information sciences. It is necessary to encrypt the vector spatial data based on the consideration of data security. The existing method is to encrypt the entire data file, which affects the view of attribute data and destroys vector spatial data structure. Methods A coordinate encryption method which does not change the structure of vector spatial data file is proposed, which not only protects the security of data, but also has no impact on the normal use of data. Firstly, vector spatial data are encrypted in Haar transform domain by the secret key. The secret key used to encrypt the coordinate data is generated combined Hash key with Gaussian random number. The average coefficient and the differential coefficient are obtained from Haar transformation of the vertex sequence of vector spatial data and are encrypted by the secret key. After that, the encrypted coordinates are obtained by Haar inverse transformation of data. Finally, the Gaussian random number is utilized to scramble the vertex sequence in order to get the encrypted disordered vector spatial data. Results The experimental results show that: (1)The structure and attribute data of the vector spatial data file are completely unchanged after encryption, only the coordinates are encrypted, and efficiency is improved. (2) The algorithm can effectively decrypt and restore the original vector spatial data, with high security. Conclusions The proposed method can not change the structure of vector spatial data, and the efficiency of encryption is improved.
2022,
47(11):
1956-1962.
doi: 10.13203/j.whugis20200265
Abstract:
Objectives Aiming at the denoising problem of acceleration signal in gait recognition method, a parameter optimization method using composite evaluation index and wavelet entropy for wavelet denoising of gait acceleration signal is proposed. Methods Firstly, based on the geometric and physical meanings of the four traditional evaluation indexes of wavelet denoising effect, namely root mean square error (RMSE), signal-to-noise ratio(SNR), correlation coefficient (R), smoothness (r), an improved entropy weight method is used to evaluate the denoising effect. The two indexes under different wavelet decomposition levels are linearly weighted to obtain a new comprehensive evaluation index. The decomposition level corresponding to the lowest point of the comprehensive evaluation index is the optimal decomposition level. Then, based on the physical meaning of wavelet entropy, the optimal wavelet base for wavelet decomposition is determined by calculating the wavelet entropy of the low-frequency coefficients of different wavelet bases to process the signal. Results Simulation and test results show that: (1) Compared with the existing composite evaluation indexes, the composite evaluation index has certain advantages in reliability, simplicity and accuracy; (2) wavelet entropy based wavelet selection scheme (WEBWSS). takes into account the optimization of the number of decomposition layers and the wavelet basis, and has better denoising performance than WEBWSS; (3) The discriminant function method after optimization can locate the singularity of the signal well, but at the cost of the smoothness of the denoising signal. Under the premise of meeting the filtering requirements of gait signal research, the detailed features of the wavelet denoised acceleration signal waveform obtained by the WEBWSS are well preserved, and the gait cycle can be clearly distinguished. Conclusions The wavelet denoising scheme determined by optimized WEBWSS can obtain satisfactory results when processing gait acceleration signals.
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