2017 Vol. 42, No. 6
Based on the DMSP/OLS nighttime light data for the years 1993-2012 and spatial analysis methods including standard deviational ellipse and rank-size distribution, this paper systematically analyzes the spatial structure and spatiotemporal dynamics of the urban system in countries along B & R (The Belt and Road Initiative). We found that nighttime light increased in most countries along B & R. These fast growing countries are undergoing economic reforms and post-war reconstruction, while nighttime light reduction occurs in areas of social and economic unrest. The trend of size distribution of nighttime light in B&R is continuous spatial expansion, and the center of the nighttime light is moving to southeast Asia. The nighttime light distribution in the top 2000 urban places in B&R follows the rank-size distribution, thus urban land distribution is more concentrated than the past. The high rank cities are fairly well developed, but the development of the small cities is lagging behind. The general distribution trend toward, concentration is stronger than decentralization in B&R.
The harmony of the whole and the local layout is the key to generating schematic network maps. The existing methods simply take line segments as the basic unit for schematization and easily cause global inconsistency or local deformation of schematic results when spatial distribution is not balanced. Considering the properties of connectivity and closure of network structures, this paper presents a new method to generate schematic maps by network simplification and partition. The core idea is simplifying network structures into meshes and lines. Firstly, the number of nodes are reduced and the direction and length of lines are adjusted to form a consistent simplification network. Secondly, the network structure is partitioned into a mesh set and line set. Thirdly, meshes and lines are schematized, avoiding local congestion by mesh schematization. An experiment shows the performance of our method under different constraints and makes comparisons with the classical iteration optimization method. Results indicate the advantages of our method in preserving the overall network shape and local element arrangements.
A variety of indoor positioning technologies have emerged, such as ultrasound, infrared, wireless local area network, bluetooth, radio frequency identification, and ultra wideband. These systems require additional hardware devices and complicated deployment. Pedestrian Dead Reckoning can provide motion information at a high update rate and achieve high precision over a short time duration, However, without external aids, the system suffers from local anomalies and cumulative error after positioning for a longer length of time. Many researchers use a step length model combined with direction information to calculate the displacement of indoor pedestrians with accuracy step counting method. A step counting algorithm based on zero-velocity update was proposed. By analyzing the pedestrian's moving character and posture, acceleration amplitude measurement used to detection the movement stage of pedestrian was employed to calculate steps with Micro Electro Mechaical System (MEMS). The experimental results show that the scheme proposed in this paper was high precise and had good adaptability to different motion state, reporting accurately more than 98% overall, which is appropriate for complex indoor environment.
In the study of geographical spatial autocorrelation, the spatial weights matrix is regarded as a fundamental and essential field area of research. In this paper, we firstly analyze the advantages and disadvantages of several common weights matrixes like ROOK matrix, QUEEN matrix and K-Nearest matrix. Considering the improvements for in the traditional spatial weights matrix, we put forward RLA (Ratio of Length and Area) which isthat can be applied with a stricter measurement standards and representsing the relative relationship of a spatial unit to other adjacent ones units instead of a simple true or false discrimination to improve the accuracy. For verification, we carry carried out experiments on the Mainland China viral hepatitis statistical data from 2004 to 2012 of Mainland China based on the provincial administrative divisions. The rResults indicate that the proposed weights matrix not only achieves the fundamental functions of a spatial weights matrix, but also is treated as a general definition of ROOK matrix, freely applicable to when implementing spatial autocorrelation analysis. The adoption of this RLA spatial weights matrix will further reveal the spatial relationships among different geographical units, also providing support for the prevention of epidemics.
Consistency is an important criterion of spatial data quality. Checking the inconsistencies is critical for maintaining the integrity of multi-resolution or multi-source spatial data. Annotation is an important element of the map, which contain abundant geographical location information. Multi-resolution spatial data always contain the inconsistencies of annotations due to measurement methods, data acquisition approaches, and map generalization algorithms. In this paper, we try to use a new kind of raster data which named map tile to detect the consistency information of multi-scale annotations. On the basis of existing space target change type, we put forward 12 types of basic changes, quantify the annotation consistency between the different of map, and put forward the methods of consistency detection for raster map annotation. We use the methods to detect and measure the consistency of annotations in web raster map, it is proved that the methods are reasonable and effective.
To deal with the problem of confused display in 3D visualization of a flow field, an adaptive visualization method for flow fields is put forward. Due to the complicated feature in flow field, a fuzzy support vector machine is applied to describe and cluster the flow field features. To resolve the overlapping effects caused by excessive noise or omission of details caused when noise is removed, an improved line integral convolution algorithm is also presented. In the improved algorithm a fuzzy membership function obtained by fuzzy support vector machine generates sparse noise adaptively Experimental results illustrate the efficiency of the method.
Existing spatial object shape similarity matching methods are not accurates. To solve this problem, a spatial object shape matching method based on triangular division is proposed. This method segments the areal spatial object through the main direction of the object shape and divides the object shape into triangles into a series form, parallel connection form, and combined form. This method describes the shape features of the areal spatial object exactly and measures the shape similarity of spatial objects. The matching on the shape data set, the matching on areal water in different years and sketch retrieval on a vector map are used to test the retrieval performance of this method. This method is compared to other spatial object shape matching methods. Experimental results show that this method has higher retrieval accuracy. The spatial object shape matching method based on triangular division is invariant to translation, rotation, scaling, and has a strong capacirty to describe and recognize shapes.
Quantitative evaluation of the uncertainty in spatial topological relations can provide the reliable basis for applications in automatic evaluation of multi-scale topological relation consistency, spatial reasoning, and spatial queries. A geometric measure-based topological distance was defined. A rough set model for uncertainty in topological relation proposed to explore a quantitative method for determining topological distance. Furthermore, this paper presents the measure index for uncertainty in spatial topological relations of multi-scale spatial entities. A case study shows that the model proposed in this paper is reasonable, and suitable for the quantitative evaluation of topological relations found in multi-scale representation processes.
The automatic recognition of bridges has both civil and military significance. However, in complicated cases when the image resolution is at the decimeter scale. the bridge scenes are messy and the targets small, and automatic recognition will become quite complicated. Thus, we proposed a novel algorithm based on the analysis of the statistical distribution and features of bridge targets in high-resolution SAR images. A CFAR detector locates potential bridge targets based on the Weibull distribution. Scene areas of bridges are extracted and false alarms are removed by utilizing the features of bridges with the help of Hough transformation. Domestic airborne polarimetric SAR data and AIRSAR data illustrate the effectiveness of this method. Results indicate that this algorithm recognizes bridges in complicated cases with high adaptability.
Remote sensing (RS) images are an important source of geospatial data. However, current approaches in task-driven RS images discovery establish links between tasks and RS image parameters directly, without spatial-temporal constraints, leading to hard maintenance and low query precision Moreover, the complex relationship between tasks and RS images under spatial-temporal constraints is difficult to model and represent by rules. Thus, this research proposes an location and time method that not only filters but also acts as spatial-temporal constraint in the discovery process, and exploits the relationships between tasks and RS data sources under spatial-temporal constraints through Case-based Reasoning (CBR). The RS application case representation model and similarity assessment model is proposed to support analogical reasoning in CBR. A prototype system was developed to validate this method. The results show that the method is a feasible approach that improves the service efficacy of remote sensing data.
The road tracking method based on template matching is one major semi-automatic road extraction method. However, template matching is sensitive to complexity of road scenes and variance in road width. In addition, road extraction requires frequent human-computer interaction while road tracking encounters failure without a mechanism for re-detection. To solve these problems, one semi-automatic road extraction method using high resolution remote sensing image based on P-N learning is proposed. It consists of road tracking, detecting and learning. In order to improve the stability of road detection, we train a classifier with an iterative P-N learning strategy. The performance of classifier is improved by correcting sample labeling under structural constraints. In experiments, the proposed method and three classical methods are tested on high-resolution remote sensing images of different scenes. Comparitive results show proposed method' improves precision and stability of road extraction.
Domestic earth observation satellites, such as ZY-3 and GF-1, have few image bands and limited spectral range. Focusing on those features, this paper outlines a cloud detection algorithm that automatically obtains the grey threshold through histogram fitting by the Gaussian Mixture Model. The algorithm obtains initial parameters automatically from the image histogram; and then it adjusts these parameters by iteration based on the Expectation Maximum principle. It automatically obtains the grey threshold between cloud and clear sky in a chosen image, in line with the distribution features of the components in the Gaussian Mixture Model. Experimental results show that this method has strong advantages, as the range of the spectral bands are not a limit and thus is suitable for both cloudy and cloudless images. In addition, the proposed method needs no auxillary information or manual intervention. It has high computational accuracy and efficiency meeting the requirements for automatic engineering production.
Total ozone data plays is essential for monitoring the spatial distribution and temporal change in the total ozone. However, since late 2006, total ozone production from OMI (ozone monitoring instrument) aboard on Aura OMTO3e satellite began show defects resulting from an OMI instrument anomaly. The missing parts accounting for one third or even more of one set data, which is a serious obstacle for normal usage. Therefore, the recovery pf missing OMTO3e data is necessary. In this paper, a multi-temporal fitting model, based on the characteristics of OMTO3e data, is proposed to reconstruct the missing data. Experimental results and comparitive quantitative evaluation, verify that the proposed method has higher feasibility and superior to traditional methods.
A method for estimating the land subsidence velocity field was proposed by combining m ulti-platform InSARdata set. The precise estimation of subsidence could be achieved by combing redun-dant observations.However, the key problems to be solved in integration of different SAR datasetsincluded different imaging geo metries and inconsistent spatial/tem poral resolutions.We discussed allthese problems in detail and given one solution. Our method then was applied to detect unbiased long-term velocity field in Shanghai, China with 18 Terra SAR-X、16 ENVISAT ASARand 20 ALOS PAL-SAR datasets. Firstly, large-scale velocity maps from three SAR data stacks were extracted whichshow similar deformation patterns and different nu merical ranges. Then, weighted least squares ad-justment was used to derive unbiased velocity field. The experimental results were validated with lev-eling data. The experiment results show that combing multi-platform InSA R data achieved precise es-timation of deformation without any prioriinformation.
The SAR/interferometric radar altimeter (SIARL) on the CryoSat-2 platform is designed to accurately determine the height changes in the Earth's continental and marine ice fields. In addition, its synthetic aperture radar interferometry mode (SARIn) is capable of providing precise three-dimensional measurements. However, SARIn level 2 products provided by the European Space Agency (ESA) do not fully utilize interferometric information. In this paper, the basic principles of the SARIn mode are introduced. By integrating the traditional interferometric synthetic aperture radar (InSAR) technique, a processing scheme is proposed for SARIn level 1b (L1b) data to extract digital elevation models (DEMs). This processing scheme uncludes three steps. Firstly, checking the quality of the input data eliminates erroneous information. Secondly, a starting point for phase unwrapping is determined, based on the magnitude and the coherence of the received signals and a targeted algorithm is accirdingly designed and implemented to unwrap the interferometric phase along the across-track direction on a line-by-line basis. Thirdly, the look angle of the satellite is calculated and used to estimate the 3D information of ground points. After a process of interpolation, DEM are generated. With the use of the proposed scheme, the SARIn L1b data acquired between January 2012 and April 2012 were processed. The ground elevation of Lambert Glacier in Antarctic was mapped and compared to the ICESat DEM and RAMP DEM. The results demonstrate that a DEM generated based on SARIn data can satisfy the research requirements for ice cap mapping in polar areas.
This paper focuses on using ERA-Interim atmosphere data and de-smoothing spherical harmonic analysis method to compute Gravity Recovery And Climate Experiment atmospheric de-aliasing models based on pressure jumps found in the European Centre for Medium-range Weather Forecast operational analysis atmosphere data, resulting from change of horizontal and vertical resolution, The computed model and the Atmospheric and Oceanic De-aliasing level 1B RL05 atmospheric model are compared, in view of the spectral and spatial domains. The Principal Component Analysis method was used to do the comparison. Moreover, the Root Mean Square of the range rate residuals was also used as a criterion to evaluate the quality of these two models. The results show that the two models have similar precision. The differences between these two models is negligible when computing GRACE temporal gravity field models, but the difference should be considered when computing next generation satellite temporal gravity field models.
Solar Radiation pressure is one of significant perturbation factors in the calculation of dynamic satellites orbits, especially for the mid-high orbit satellites. In order to remove the effects of radiation pressure on mid-high orbit satellites, researchers have developed various radiation pressure models, including empirical and analytical models. According to their advantages or disadvantages and the real satellite space environment, this paper establishes a model for complex structure satellites combining prior sampling satellites in an analytical model with self-correcting method in operation, which approximates the real radiation pressure circumstances. The simulation tests and analysis show good results in both orbit determination and self-correction.
Using high precision GPS data for the period of 2001-2010 of Weihe basin, adopting the finite element numerical analysis method, we obtained the current tectonic stress fields and activities characteristics of deep faults in the Weihe basin. These results show that: the deep faults in western basin present significant compressive stress characteristics in the nearly E-W direction. In central area, obvious tensile stress appears in the NW-SE direction, in east deep faults also present certain tensile stress in the NW-SE direction, but the magnitude is smaller than the central area. Even for the same deep fault with different trends, in different sections, the stress characteristics, values, and activity rates are divergent. The velocity components along the strike of the deep faults show certain changes at both ends of the faults, revealing deep faults in the Weihe basin with twist characteristics. The results further reveal that compared to western and eastern basin, the central area is significantly influenced by the tensional stress in the NW-SE direction. These areas are where deep faults intersect, with tensile stress in central basin, especially in sections with higher movement rates; they show the most developed ground fissure hazards.
Satellite Laser Ranging (SLR), is completely independent of microwave measurement, and offers an indispensable tool for external validation of GNSS broadcast ephemeris. Currently, all satellites in the BeiDou Satellite Navigation System (BDS) are equipped with laser ranging reflector arrays enabling high-precision two way ranging measurements. Based on SLR data from BeiDou satellites collected in the period April 2013 to July 2014, the broadcast ephemeris of BDS were validated using SLR data. The approximate equation of SLR residual for the BDS GEO satellites were derived, and the SLR residual characteristic for BDS IGSO and MEO satellites under different attitude modes were analyzed. The validation results show that, the orbit error of C01, C08, C10 and C11 are 0.97 m, 0.43 m, 0.41 m and 0.41 m respectively.
With reference to the accuracy and time of rapid and ultra-rapid satellite orbit and the unbalanced distribution of global tracking stations, GNSS Data Analysis Centers are meeting with big challenge. This paper proposes an optimal stations selected model called SSS (selected step by step) which is based on the GDOP (geometric dilution of precision) value of observation equation. Firstly, the calculation methods of optimal and the minimum of ground tracking stations for precise orbit determination were deduced. Secondly, according to the global grid of s°×s°and k°×k°, the distribution of minimum stations were selected out. Thirdly, based on the standard of minimum GDOP, an optimal distribution of global tracking stations was gradually accumulated step by step. Six days continuous experiment shows, on the same numerical computation ability, SSS model can reach 90% level of accuracy as the whole data processing and reduce computation time at less 50%. Comparing with the ordinary methods, it shows that SSS model can get the same accuracy as the ordinary methods, while could save time reaching up 20%. Moreover, several comparison experiments indicated that SSS is the optimal or sub-optimal model for the station selected and improves the efficient of data analysis centers.
Cycle-slip detection and correction is an important issue in high precision kinematic GPS positioning, which seriously affects the efficiency of solving ambiguity on-the-fly. A new algorithm avoiding the shortcomings of triple-differenced methods to detect and repair cycle-slip based on double differenced model between receivers and epochs is proposed. First, outlier detection is used to locate the possible cycle-slip affected double difference, carrier phase observations, and the initial cycle-slip value can be determined. Finally, a cycle-slip optional combination search approach, based on the principle of least sum of squared residuals, was adopted to repair the cycle-slip. Theoretical analysis and experimental results show that cycle-slips can be located and repaired accurately with this new algorithm in most cases, when the number of effective observed satellites was not less than four.
Along with the great improvement of the image spatial resolution of the geostationary satellite, elevation correction of geostationary satellite is increasingly attractive. According to the projective mode and imaging pattern of new generation geostationary satellites, in order to address the non-convergent problem caused by traditional iterative searching algorithm, a new algorithm which searches the occlusive points directly on the observing vectors is proposed. Then, a geostationary satellite elevation correction strategy which combines the strong stability of direct searching algorithm and high computational efficiency of iterative searching algorithm is developed. The simulative experiments show that the proposed strategy is valid.
As a prior stochastic model contains inaccurate information, the weight matrices of observation and coefficient matrix are unreasonable. To address this problem, we investigate the total least squares adjustment of partial errors-in-variables(PEIV)model with a weight scaling factor that adaptively adjusts the contribution of the observation and coefficient matrix to parameter estimation. A prior unite weight variance and minimum discriminate function method are deduced, so the proposed method is valid for a structured coefficient matrix. Some conclusions are drawn from simulations of straight line fitting and coordinate transformation. When the prior unit weight variances of observation and coefficient matrix are known and accurate, the prior unit weight variance method is very effective; the minimum discriminate function method with the
The spot influences the positional accuracy of terrestrial laser scanning. A Laser point in the spot is uncertain and the description of this uncertainty directly relates to the evaluation of positional accuracy. This paper introduces error entropy into the evaluation of point cloud uncertainty related to the spot. Point information entropy is deduced by using The probability density function of the laser point in the spot; and error entropy is obtained based on the relationship between error entropy and information entropy and evident in the relationship between error entropy and spot size; introducing average error entropy into the evaluation of point cloud uncertainty. The evaluation of average error entropy as a measure of point cloud accuracy influenced by laser spot was verified by an analysis of different scanning intervals, thus an evaluation of point cloud uncertainty influenced by the spot was realized.