Table of Content

20 December 2018, Volume 47 Issue 12

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Several Kinematic Data Processing Methods for Time-correlated Observations

LI Bofeng, ZHANG Zhetao

2018, 47(12):  1563-1570.  doi:10.11947/j.AGCS.2018.20180192

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Time correlations always exist in modern geodetic data, and ignoring these time correlations will affect the precision and reliability of solutions. In this paper, several kinematic data processing methods for time-correlated observations are studied. Firstly, the method for processing the time-correlated observations is expanded and unified. Based on the theory of maximum a posteriori estimation, the third idea is proposed. Two types of situations with and without common parameters are both investigated by using the decorrelation transformation, differential transformation and maximum a posteriori estimation solutions. Besides, the characteristics and equivalence of above three methods are studied. Secondly, in order to balance the computational efficiency in real applications and meantime effectively capture the time correlations, the corresponding reduced forms based on the autocorrelation function are deduced. Finally, with GPS real data, the correctness and practicability of derived formulae are evaluated.

Determination of Smoothing Factor for the Co-seismic Slip Distribution Inversion

WANG Leyang, ZHAO Xiong

2018, 47(12):  1571-1580.  doi:10.11947/j.AGCS.2018.20170724

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For the determination of the smoothing factor (also known as the regularization parameter) in the co-seismic slip distribution inversion, the compromise curve between the model roughness and the data fitting residual is generally used to determine (in order to distinguish the method proposed in this paper, the method is called "L curve" according to its shape). Based on the L-curve, the eclectic intersection curve as a new method are proposed to determine the smoothing factor in this paper. The results of the simulated experiment show that the inversion accuracy of the parameters of the seismic slip distribution with the smoothing factor determined by the eclectic intersection curve method is better than that of the L curve method. Moreover, the eclectic intersection curve method and the L curve method are used to determine the smoothing factor of L'Aquila and Taiwan Meinong earthquake slip distribution inversion respectively, and the inversion results are compared and analyzed. The analysis results show that the L'Aquila and Meinong of Taiwan actual earthquake slip distribution results are in the range of other scholars at home and abroad, and compared with the L curve method, the eclectic intersection curve method has advantages of high computation efficiency, no need to depend on data fitting degree and more appropriate of smoothing factor and so on.

An Analysis on the Construction of Large-scale Dynamic Changing Model of Discrete Time Variable Gravity from GRACE

ZHU Chuandong, LIU Jinzhao, WANG Tongqing, CHEN Zhaohui, ZHANG Pin, ZHANG Shuangxi

2018, 47(12):  1581-1590.  doi:10.11947/j.AGCS.2018.20180167

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For the monthly spherical harmonic coefficients (SHCs) from GRACE, a method using empirical orthogonal function (EOF) and multi-channel singular spectrum analysis (MSSA) is presented to effectively reduce its dimension and remove its strip noise. The dynamic changing of the SHCs is analyzed, and the corresponding changing model is then constructed. The significant annual term and long term trend have been found in the discrete SHCs, which account for 50.6% and 77.4% (after the annual term has been deducted) of the total variance, respectively. The nonlinear model, which constructed from long term trend and annual term, can perfectly reflect the dynamic changing of the SHCs and its inversion results. And the fitting standard deviation of the equivalent water height on grids over global land areas is between 0.3 cm and 14.0 cm, and the average standard deviation is 1.8 cm. This study can provide valuable implications for the research on the parameter changing characteristics of gravity field and geodynamics.

Generating Carrier Range with Between-satellite Single-difference Phase Ambiguity Resolution

RUAN Rengui, WEI Ziqing, JIA Xiaolin

2018, 47(12):  1591-1598.  doi:10.11947/j.AGCS.2018.20180214

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Data processing for large-scale GNSS network is faced with increasing challenges as both the number of tracking stations and navigation satellites continuously increases. It has been shown that converting original carrier phase observations to carrier range observations is one of the valid approaches to improve the computing efficiency of data processing. In this paper, a new method to generate the carrier range observation is presented,correcting the ionosphere-free combination of carrier phase using the estimation of un-difference ambiguities obtained in the PPP solution with fixing between-satellite single difference ambiguities. Experiments with GPS data from the crustal movement observation network of China (CMONOC) during day 1-30 of year 2017 are conducted to validate the proposed approach. It is demonstrated that, using the carrier range observation, the computation time for the network with 252 stations is less than 20 minutes. If the original phase observations are used, it takes about 11 hours, nearly half of which is spent for resolving integer double difference ambiguities. Excluding the 12 abnormal stations, the monthly coordinate repeatability of the 240 stations are 0.74, 0.85 and 2.53 mm on average respectively in the directions of N, E and U, which are slightly better than those with original phase data. We also discuss the difference of integrated network solutions with original phase and carrier range. Using the concept of adjustment model with constrain condition, a unified formula of observation model is presented to interpret the principle of integrated network solution with carrier range generated with various integer ambiguity resolution strategies, i.e. resolving zero-difference, double-difference and between-satellite single-difference integer ambiguities. It is concluded that the effect of network solution with carrier range observation is theoretically equivalent with the traditional approach with original phase data.

The Impact of Yaw Attitude of Eclipsing GPS/GALILEO Satellites on Kinematic PPP Solutions and Their Correction Models

LIU Tianjun, WANG Jian, CAO Xinyun, KUANG Kaifa, FAN Caoming

2018, 47(12):  1599-1608.  doi:10.11947/j.AGCS.2018.20170695

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When GPS/GALILEO satellite runs to the position where it is approximately collinear with the sun and the earth, it is difficult for the satellite to keep nominal attitude, so it will show abnormal yaw attitude for a period of time. Based on the precision orbit and clock correction products offered by different analysis centers, we design different attitude correction strategies for satellite that is in abnormal yaw attitude period, select 10-day measured data from 7 MGEX stations distributed globally, and analyze the influence of antenna phase center offset and phase wind-up of GPS/GALILEO satellite on residuals of observations and kinematic PPP positioning result in this paper. The research results show that when the satellite is in abnormal yaw attitude period, adopting nominal yaw attitude can have an impact up to 8 cm and 11 cm on the residuals of GPS/GALILEO satellite observations. GPS/GALILEO satellite is in model yaw attitude during the period and its positioning accuracy of kinematic PPP positioning results in three directions of E, N and U shows an increasing rate of 13.30%, 15.77% and 12.98%, respectively in comparison with that in nominal yaw attitude. Comparing with satellite deletion strategy, the accuracy of kinematic PPP positioning results in three directions of E, N and U when the satellite is in model yaw attitude shows an increasing rate of 5.399%, 4.430% and 5.992%, respectively.

Polarimetric SAR Sea Ice Classification Based on Target Decompositional Features

ZHAO Quanhua, GUO Shibo, LI Xiaoli, LI Yu

2018, 47(12):  1609-1620.  doi:10.11947/j.AGCS.2018.20170551

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Feature extraction and its selection are one of the most important steps in the SAR sea ice classification. The key to improve the classification accuracy is to select effective features and to construct the feature space that effectively expresses the type of ground objects. For this purpose, a full polarimetric SAR sea ice classification algorithm based on target decomposition features is proposed in this paper. First of all, multilook process and filter operation are preformed to full-pol SAR data and result in coherency matrix. Secondly, in order to construct the feature space, target decomposition on coherency matrix is employed to extract related scattering feature parameters. Thirdly, after analysis of statistical correlation about extracting features, PCA feature reduction operation is carried out on those higher relevant features for the purpose of optimizing the combination of features. Finally, a BP neural network-based classification algorithm is designed to classify sea ice, and the optimization of the feature vector as input layer, the class of sea ice as output layer. In experiment, the central Greenland area is regard as the research area and L-band ALOS PALSAR full polarimetric data are utilized as experimental data. Through the qualitative and quantitative analysis for the proposed and comparing algorithms, it can be found that the feature space built up can efficiently distinguish various sea ices. Furthermore, by analyzing the performance of sea ice classification results with different feature combination, we can conclude that the features of the target decomposition based on scattering model can provide a better capability to identify water and sea ices compared to H/α/A decomposition based on eigenvalue.

Variational Uneven Illumination Correction with Double-norm Hybrid Constraints for Remote Sensing Imagery

LI Shuo, WANG Hui, GENG Zexun, YU Xiangzhou, LU Lanxin

2018, 47(12):  1621-1629.  doi:10.11947/j.AGCS.2018.20170625

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In response to the uneven illumination of remote sensing image, a variational method of uneven illumination correction with double-norm hybrid constraints is proposed. Firstly, the deficiency of the variational method with single norm constraint is analyzed. A new variational model is constructed according to the characteristics of the reflectance image and the illumination image. The L₁ norm and the L₂ norm are adopted to constraint the texture and detail of reflectance image and the smoothness of the illumination image, respectively. Then, the solution of the variational model is translated into three sub-problems with split Bregman method and calculated using alternate iteration method to accelerate the computation. The mean of the illumination image enhanced with Gamma correction method is multiplied by the reflectance image. Finally, the contrast stretch transformation is implemented for a better performance. Experimental results indicate that the proposed method is practical and feasible. Compared with the variational Retinex method with single norm constraint, the proposed method performs better in correcting the uneven illumination and preserving the textures and details of the remote sensing image. In addition, the proposed method is more efficient, of which the running time is less than one seventh of that of the variational Retinex method with single norm constraint.

Right-angle Buildings Extraction from High-resolution Aerial Image Based on Multi-stars Constraint Segmentation and Regularization

DING Yazhou, FENG Fajie, LI Junping, HU Yan, CUI Weihong

2018, 47(12):  1630-1639.  doi:10.11947/j.AGCS.2018.20170486

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This paper proposes a method of semi-automatic right-angle building extraction from very high resolution remote sensing imagery, based on graph cuts with the multi-stars shape constraint and regularization. The framework consists of the following steps:Firstly, the image block containing the target building is obtained by manual interaction. Next, the image block is preprocessed by bilateral filtering. Then the graph cuts with the star shape constraint is used to obtain the building objects.Finally, building object is regularized into real regular shape through corner detection and linear fitting. The experiments performed on two different region and spatial resolution aerial imageries demonstrate the stability and accuracy of the proposed method.

External Parameter Calibration Method of Vehicle Laser Scanning System Based on Planar Features

ZHANG Haixiao, ZHONG Ruofei, SUN Haili

2018, 47(12):  1640-1649.  doi:10.11947/j.AGCS.2018.20170495

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The calibration of the external parameters of the laser scanner is the precondition and guarantee for obtain high-precision 3D geographic data. Most of the traditional calibration methods require setting up a special calibration field, manual collection of checkpoints, or the amount of calculation in the process of solving is large. Based on this, an automatic calibration method is proposed in this paper, by collecting point cloud data in the same area with different vehicle directions, extracting planar features data and automating registration of these planar features data, through the co-calibration of planar features of different angles.The proposed method realizes the coincidence of point clouds collected by different vehicles in three-dimensional space and finally completes the calibration of the system external parameters. The results show that the method is automatic for the calibration of the external parameters of the vehicle laser scanning system, reduces the need for manual participation, and achieves high precision.

A Method for Road Map Construction Based on Trajectory Segmentation and Layer Fusion Using Vehicle Track Line

YANG Wei, AI Tinghua

2018, 47(12):  1650-1659.  doi:10.11947/j.AGCS.2018.20170182

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Traditional methods treat track points (lines) equally to extract road data, which ignores the spatial distribution disparity and restricts its application. Therefore, this paper proposes a new approach for map construction based on trajectory segmentation and layer fusion from vehicle tracks. First, track line subset is selected through the segmentation filtering method based on speed profile. Second, three road map layers are constructed by the Delaunay triangulation through adding different constraints according to the feature of track line subset. Third, buffer method is used to integrate multiple road layers into a single road map. An experiment using taxi GPS traces in Beijing is verified the novel method. The experimental results show that our method can extract road geometry and traffic semantic data considering the heterogeneity of trajectory, and the accuracy of result is improved compared with the two existing methods.

Predicting Future Locations with Deep Fuzzy-LSTM Network

LI Mingxiao, ZHANG Hengcai, QIU Peiyuan, CHENG Shifen, CHEN Jie, LU Feng

2018, 47(12):  1660-1669.  doi:10.11947/j.AGCS.2018.20170268

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Current studies on trajectory prediction have two limitations. Spatial division approaches in most existing studies lead to sharp boundary problem of predicting methods. On the other hand, most of traditional predicting models such as Markov could only use a few latest historical locations, making long-term prediction inaccurate. To overcome these limitations,a location prediction method based on deepFuzzy-LSTM Network is proposed. The method employs a long short term memory based network to solve the long-term dependencies problem. By defining the fuzzy-based trajectory and the improved LSTM cell structure, our method solves the sharp boundary problem caused by space partition. It also considers both period and closeness of movement patterns in making prediction. We compare classical NLPMM, Naïve-LSTM and Fuzzy-LSTM methods with a cell signaling dataset consisting of the continuous trajectories of one hundred thousand city residents in 15 workdays. Results show that the proposed Fuzzy-LSTM method gets a precision of 83.98%, 6.95% higher than the NLPMM model and 4.36% higher than Naïve-LSTM model.

Roundabout Recognition Method Based on Improved Hough Transform in Road Networks

CUI Xiaojie, WANG Jiayao, GONG Xianyong, WU Fang

2018, 47(12):  1670-1679.  doi:10.11947/j.AGCS.2018.20170736

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Spatial distribution pattern is significant to the map generalization and map matching. The roundabout is the typical representative of miniature ring-like pattern in road networks. Based on the principle of improved Hough transform to detect the circle, a geometric recognition method of roundabout is proposed in this paper. This method can be divided into two parts:circulating road recognition and branch recognition. Firstly, circulating road is identified by the circle recognition, uniformity optimization, and similarity optimization. Then the branch is identified by connectivity discrimination, branch classification and combined branch supplementation. The results of partial road data in UK show that the proposed method can effectively identify the roundabouts, and both the recall and precision are higher than the comparison method.

Urban Expansion Model Based on Extreme Learning Machine

WANG He, ZENG Yongnian

2018, 47(12):  1680-1690.  doi:10.11947/j.AGCS.2018.20170586

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Urban space structure and its simulation are important prerequisites for urban scientific management and planning. Based on the extreme learning machine, this paper proposes an urban extended cellular automaton model (ELM-CA) that takes into account the differences and intensities of different non-urban land conversions into urban land use. The experimental results show that the urban simulation accuracy of ELM-CA model reaches 70.30%, which is 2.21% and 1.54% higher than logistic regression and neural network respectively. The FoM coefficient is increased by 0.025 9 and 0.017 9 respectively, and the Kappa coefficient is improved by 0.024 7 and 0.016 9 respectively. And the Moran I index is close to the actual value, which shows that the extreme learning machine model can simulate and predict the spatial shape and change of urban expansion more effectively than logistic regression and neural network; the training time of ELM model is only about 1/3 of the neural network, it reflects the advantage of ELM learning speed; In the small sample case, both logistic regression and neural network are significantly affected, and the extreme learning machine can maintain good performance, which makes it have obvious advantages when the sample is difficult to obtain. The comparison between urban expansion simulation and real data of two phases shows that the urban extended cellular automata model (ELM-CA) based on the extreme learning machine simplifies the complexity of the CA model and can effectively improve simulation accuracy under small sample conditions. The proposed model is suitable for urban expansion simulation and prediction under complex land use conditions.

Earth Observing Satellite Laser Altimeter Data Processing Method and Engineer Practice

LI Guoyuan

2018, 47(12):  1691-1691.  doi:10.11947/j.AGCS.2018.20170681

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Study on the 3D Cadastral Spatial Data Model Based on Geometric Algebra

ZHANG Jiyi

2018, 47(12):  1692-1692.  doi:10.11947/j.AGCS.2018.20170725

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Spatial Alignment of Multi-platform Point Clouds and On-demand 3D Modeling

ZANG Yufu

2018, 47(12):  1693-1693.  doi:10.11947/j.AGCS.2018.20170730

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Interseismic Strain Accumulation Across the Western Altyn Tagh Fault from Wide-swath InSAR Observations

LI Peng

2018, 47(12):  1694-1694.  doi:10.11947/j.AGCS.2018.20170731

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Research on Methods of Generating Stereo Panorama of Geographical Scene Based on Video Sequence

LI Jia

2018, 47(12):  1695-1695.  doi:10.11947/j.AGCS.2018.20180002

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Anisotropy Radial Basis Function Spatial Interpolation Model Research for 3D Spatial Field

DUAN Ping

2018, 47(12):  1696-1696.  doi:10.11947/j.AGCS.2018.20180008

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