Table of Content

20 December 2020, Volume 49 Issue 12

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Geodesy and Navigation

Evaluation and analysis of stochastic modeling of BeiDou GEO/IGSO/MEO satellite observation

GAO Weiguang, MIAO Weikai, CHEN Gucang, JIA Song

2020, 49(12):  1511-1522.  doi:10.11947/j.AGCS.2020.20190492

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Firstly, the importance of stochastic model in precise positioning is demonstrated from the perspectives of parameter estimation, accuracy evaluation and quality control. Then, based on the single-difference functional model, the rigorous variance component estimation (VCE) method is used to allow the estimation of satellite-specific variances, cross correlations between two arbitrary frequencies, as well as the time correlations for phase and c+ode observations per frequency. The influence of the stochastic model on baseline precisions and the overall statistics was subsequently analyzed. The results show that the observation precisions of the BeiDou user receiver are overall elevation-dependent for phase and code of all frequencies. It is recommended to use the elevation-dependent exponent weighting model; there are different degrees of correlation between the three frequency phase observations, and the cross correlation between other types of observations is not obvious. The time correlation between phase and code observations of different frequencies is obvious, and attention should be paid to high-precision positioning. Furthermore,the baseline precisions that used the correct stochastic model match the theoretical ones very well for the three baseline components. The paper provides support for users to correctly understand the three types of satellite observation of the BeiDou system and correctly apply the BeiDou system.

GPS-based space-surface passive bistatic radar technique for maritime moving target detection

HE Zhenyu, CHEN Wu, YANG Yang

2020, 49(12):  1523-1534.  doi:10.11947/j.AGCS.2020.20190487

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GNSS satellites as an illuminator and the receiver on the ground can construct a space-surface passive bistatic radar system for maritime moving target detection. However, the GNSS reflected signals from the maritime moving targets (such as vessels) are usually very weak and submerged by the background noise and the interference. To address this issue, a target detection method is proposed, which uses the peculiarity of the target's motion for echo energy concentration. First, because the trajectory of the moving vessel is considered as a synthetic aperture, SAR imaging technique—range-Doppler algorithm (RDA) is employed to concentrate the echo energy and suppress the interference such as sea clutter. Then, phase gradient autofocus (PGA) is exploited to perform autofocusing processing for further echo energy concentration. Multiple groups of real data are collected from the field trials to validate the proposed method. The experimental results show that the proposed method can concentrate the echo energy of multiple targets, estimate the target-to-receiver range accurately, and determine the target’s moving direction.

A constant gradient sound ray tracing underwater positioning algorithm considering incident beam angle

XIN Mingzhen, YANG Fanlin, XUE Shuqiang, WANG Zhenjie, HAN Yunfeng

2020, 49(12):  1535-1542.  doi:10.11947/j.AGCS.2020.20190518

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The spatial and temporal variation of sound velocity in sea water can make sound waves refract along the propagation direction, and effective elimination of refraction artifacts is very important to improve the accuracy of underwater acoustic positioning. When the sound velocity profile is known, sound ray tracing is an effective method to reduce the refraction artifacts. The existing sound ray tracing methods require that the incident beam angle is known, while the underwater acoustic positioning systems based on the distance intersection principle usually do not directly measure the incident beam angle. A constant gradient sound ray tracing underwater positioning algorithm considering incident beam angle is proposed, which can trace sound ray and estimate target position respectively by iterative calculation, and the incident beam angle is determined by a search method. In order to improve the calculation efficiency, a transcendental equation solution method is proposed to determine the incident beam angle iteratively. The experimental results show that the proposed method effectively eliminate the effect of the refraction artifacts by using sound velocity profile, and the calculation efficiency of the transcendental equation solution method is better than the search method.

Bayesian estimation of the scale factor of relative gravimeter in precise gravity survey

WANG Linhai, CHEN Shi, ZHUANG Jiancang, LU Hongyan, ZHANG Bei, YANG Jinling

2020, 49(12):  1543-1553.  doi:10.11947/j.AGCS.2020.20200185

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The scale factor of the relative gravimeter changes slightly with time, which is an important factor affecting the accuracy of precise gravity survey. It is necessary to regularly perform a special baseline calibration on the relative gravimeter to evaluate the change of the instrument’s scale factor. This study presents a new method that can be used to evaluate the scale factor based on the gravity observation data only. The principle is to use multiple absolute gravity datum stations known in the survey network as constraints, and to take into account the nonlinear drift of the instrument, and then to estimate scale factor as one of the hyper-parameters by Bayesian theory and Akaike’s Bayesian information criterion (ABIC). Through simulation data testing, this method can obtain the accurate estimation of scale factor in the presence of uncertainties such as Gaussian noise and instrument nonlinear drift. The test of the measured gravity data shows that: the differences between the estimated scale factors and the calibration results of baseline field before measurement are within 5×10^-5, and compared with using the inaccurate calibrated scale factors, this method can obtain the better estimation of gravity values which are less different from the results of absolute gravimetry. The results of this study provide method guarantee for effectively improving the efficiency and accuracy of precise gravity survey.

Photogrammetry and Remote Sensing

Extracting urban road area based on combination of trajectory continuity and image feature similarity

FANG Zhixiang, ZHONG Haoyu, ZOU Xinyan

2020, 49(12):  1554-1563.  doi:10.11947/j.AGCS.2020.20190366

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Urban road area detecting is the imperious demand in the area of management of city land use, transportation planning and so on. Trajectory extraction, remote sensing image classification and artificial collection are the traditional methods for road network detection with some limits on automation degree or extraction quality. This paper proposes a method for detecting road area in high-resolution remote sensing image based on trajectory continuity and image feature similarity, and this method utilizes the advantages of GNSS trajectory and remote sensing image. The proposed methods could be divided into three steps: firstly, using GNSS trajectory points to construct images of trajectory feature and selecting the high-confidence grids with high density value. Secondly, generating road objects based on trajectory continuity in average direction feature image. Thirdly, dividing high-resolution remote sensing image into several small areas by using road segments and extending road areas based on image feature similarity automatically to detect roads which not covered by trajectory. The experiment evidences that this method could detect road areas efficiency and accuracy in high-resolution remote sensing image and decreasing the bad effect on the different roads with different spectrums. Compared with the traditional remote sensing image classification methods, the proposed method has a higher precision and automatic degree.

Zero-shot remote sensing image scene classification based on robust cross-domain mapping and gradual refinement of semantic space

LI Yansheng, KONG Deyu, ZHANG Yongjun, JI Zheng, XIAO Rui

2020, 49(12):  1564-1574.  doi:10.11947/j.AGCS.2020.20200139

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Zero-shot classification technology aims to acquire the ability to identify categories that do not appear in the training stage (unseen classes) by learning some categories of the data set (seen classes), which has important practical significance in the era of remote sensing big data. Until now, the zero-shot classification methods in remote sensing field pay little attention to the semantic space optimization after mapping, which results in poor classification performance. Based on this consideration, this paper proposed a zero shot remote sensing image scene classification method based on cross-domain mapping with auto-encoder and collaborative representation learning. In the supervised learning module, based on the class semantic vector of seen class and the scene image sample, the depth feature extractor learning and robust mapping from visual space to semantic space are realized. In the unsupervised learning stage, based on the class semantic vectors of all classes and the unseen remote sensing image samples, collaborative representation learning and k-nearest neighbor algorithm are used to modify the semantic vectors of unseen classes, so as to alleviate the problem of the shift of seen class semantic space and unseen class semantic space one after another and unseen after self coding cross domain mapping model mapping the shift of class semantic space and unseen class semantic space after collaborative representation. In the testing phase, based on the depth feature extractor, self coding cross domain mapping model and modified unseen class semantic vector, the classification of unseen class remote sensing image scene can be realized. We integrate a number of open remote sensing image scene data sets and build a new remote sensing image scene data set, experiments were conducted using this dataset The experimental results show that the algorithm proposed in this paper were significantly better than the existing zero shot classification method in the case of a variety of seen and unseen classes.

Landsat image glacier extraction based on context semantic segmentation network

WANG Zhongwu, WANG Zhipan, YOU Shucheng, LEI Fan, CAO Li, YANG Kaijun

2020, 49(12):  1575-1582.  doi:10.11947/j.AGCS.2020.20190313

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According to the glacier characteristics of remote sensing image, a context-aware deep learning semantic segmentation network for glacier extraction is proposed based on the glacier characteristics of remote sensing image. Firstly, resnet50 is introduced as the feature extraction network to achieve the accuracy and efficiency balance of glacier feature extraction. Secondly, the context-information learning of the existing semantic segmentation network is designed. The context information including the dilated-convolutional block and the max-pooled block is designed to better extract the context information of the glacier. Multiple remote sensing trained images and tested images are selected for experiment, which is qualitatively and quantitatively compared with the existing glacier feature index extraction method and other semantic segmentation network methods. The results show that the network method in the frozen lake surface, the leakage of the mountain shadow, cloud shadow and the integrity of the extraction results have a good effect, which verifies the effectiveness and robustness of the proposed method.

SAR and optical images registration using uniform distribution and structure description-based ASIFT

LI Dongchen, XIANG Wenhao, DANG Qiannan, WU Yan

2020, 49(12):  1583-1590.  doi:10.11947/j.AGCS.2020.20190324

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Aiming at the problems of nonlinear gray difference, speckle noise and different imaging viewpoints in SAR and optical image registration, this paper presents a SAR and optical images registration using uniform distribution and structure description-based ASIFT. In the proposed algorithm, firstly, the guided scale space is constructed by guided filter to achieve noise suppression and edge preservation. In the feature extraction stage, the phase congruency is utilized due to the nonlinear gray difference, and combined with scale space gridding to extract from images for the uniform feature points. In the feature description stage, the consistency gradient magnitude and orientation of SAR and optical image are calculated by extended phase congruency method, which improves the accuracy of the main orientation and descriptor. At last, Optimal-RANSAC is used to establish feature descriptor matching to achieve effective registration. The simulation experiment and analysis on four pairs of real images show that the proposed algorithm has more accurate registration accuracy than SAR-SIFT and traditional ASIFT.

Laser pointing changes detection method for space-borne laser spot image

YANG Xiongdan, LI Guoyuan, WANG Peixian, CHEN Jiyi, MO Fan, ME Jiaqi, JIN Zelin

2020, 49(12):  1591-1599.  doi:10.11947/j.AGCS.2020.20190423

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The positioning accuracy of the ground spot footprints is highly dependent on the positioning accuracy of the laser pointing angle. The centroid extraction and its variation law of space-borne laser spot are of important significance to the analysis of laser pointing angle. Firstly, based on the laser profile array(LPA) image data of ICESat/GLAS, this paper uses the gray-scale first-order matrix method to extract the centroid of LPA, with an accuracy better than 0.3 pixels, and the relative positioning accuracy of LPA is better than 0.11 pixels . next, Fourier transform and Fourier series fitting are used for periodic detection and modeling analysis of LPA centroid coordinates changes. The results show that the centroid coordinates of the laser spot image have four obvious periodic changes: 1.83×10^-4 Hz,3.36×10^-4 Hz,5.19×10^-4 Hz and 6.71×10^-4 Hz. The correlation R² of fitting results reached 0.86, and the fitting accuracy was up to 0.4″, which was better than 0.13 pixels, and a better result was obtained. It can provide reference for the data processing of GF-7 and the subsequent satellite laser altimeter.

Multi-dimensional convolutional network collaborative unmixing method for hyperspectral image mixed pixels

LIU Shuai, XING Guanglong

2020, 49(12):  1600-1608.  doi:10.11947/j.AGCS.2020.20190461

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Influenced by the performance of imaging spectrometer and the distribution of complex ground objects, hyperspectral images have a large number of mixed pixels. Traditional learning-based unmixing methods are shallow models, or lack of comprehensive use of spatial and spectral information. This paper proposes a collaborative deep model with multi-dimensional convolutional network. Using multi-dimensional convolutional network can make full use of multi-dimensional semantic information, which is better to estimate hyperspectral mixed pixel abundance with small samples. The method augments training data, constructs three kinds of convolutional neural networks: spectral dimension, spatial dimension and cube dimension; the method designs fusion layer to concatenate features with three kinds of convolutional neural networks, and to “end-to-end” estimate of mixed pixel abundance; the model uses batch normalization, pooling and dropout to avoid over fitting phenomenon. The experimental results indicate that the introduction of our proposed method can extract spatial-spectral feature information more effectively. Compared with other convolutional network unmixing models, the accuracy of the estimated mixed pixel abundance is significantly improved.

A DEM extraction method for vehicle-mounted interferometric SAR with dual antennas

DAI Guomeng, PAN Bin, LIU Lei

2020, 49(12):  1609-1618.  doi:10.11947/j.AGCS.2020.20200001

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Compared with spaceborne and airborne SAR, DEM extraction using vehicle-mounted SAR data can effectively shorten the update cycle of DEM data and reduce the cost. Due to the variable structure of vehicle-mounted SAR platform, interferometric calibration needs to be repeated, this paper proposes a DEM extraction method of vehicle-mounted interferometric SAR with dual antennas based on a single control point. This method corrects the phase, slope distance and elevation based on the precise coordinate information of a control point and realizes the iterative solution of elevation. It does not need to set up many control points for interferometric calibration, effectively simplifying the field work. The simulation results show that high accuracy of DEM extraction can be guaranteed when the estimation of baseline length reaches the accuracy of mm and the estimation of baseline inclination also reaches a high accuracy. Using vehicle-mounted interferometric SAR data with dual antennas obtained in wuhan, hubei province in 2018,the comparative test of DEM extraction was carried out using this method and multiple control point method based on interferometric calibration. The results show that the elevation error of this method at the checkpoints is 0.301 8 m, and multiple control point method is 0.258 4 m, the DEM results of two methods are highly consistent in the areas with high coherence.

Cartography and Geoinformation

Method of building scene structure extraction based on 2D map and its application in urban augmented reality

XU Wang, YOU Xiong, ZHANG Weiwei, DENG Chen

2020, 49(12):  1619-1629.  doi:10.11947/j.AGCS.2020.20190382

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In urban AR, the automatic acquisition of scene structure is significant to adaptive information representation, and is a key step to solve the problems of unclear information indication and confusing scene perception caused by “cascade” information representation. However, the scene structure information is hidden in the scene image, which is difficult to extract directly. The 2D map, containing the locations, contours and spatial layout of the geographic entities, can be used as the prior information of the scene structure extraction. Aiming at the problem of difficult and inefficient acquisition of scene structure in urban AR, a method of automatic extraction for building scene from image using 2D map is proposed. Firstly, on the basis of geographic registration, some structural clues for scene image semantic segmentation are constructed, according to the contours and attribute information of buildings in 2D map. Then they are used as the parameters of image merging after over-segmentation to generate multiple image regions containing semantic information. Finally, according to the mapping relationship between the image regions and the building contours in the 2D map, the scene structure information such as the region contours, scene depth and plane orientation in the scene image is extracted. 32 building scenes in Graz area were selected for testing. The results show that the proposed method can extract scene structure in real-time with high accuracy, and the quality of the building facades extraction is obviously better than that of the comparison methods. The extracted scene structure can be applied to various adaptive information expression methods in urban AR to make the information annotation and the occlusion relationship clearer.

Deep reinforcement learning based electric taxi service optimization

YE Haoyu, TU Wei, YE Hehui, MAI Ke, ZHAO Tianhong, LI Qingquan

2020, 49(12):  1630-1639.  doi:10.11947/j.AGCS.2020.20190516

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Electric taxis have been demonstrated with the promotion of electric vehicles. Compared with internal combustion engine vehicles, electric taxis spend more time in recharging, which reduces the taxi drivers’ intention to use. Reinforcement learning is applicable to the sequential decision-making process of taxis driver. This paper presents the double deep Q-learning network (DDQN) model to simulate the operation of electric taxis. According to the real-time state of taxis, DDQN will choose the optimal actions to execute. After training, we obtain a global optimal electric taxi service strategy, and finally optimize the taxi service. Using real-world taxi travel data, an experiment is conducted in Manhattan Island in New York City, USA. Results show that, comparing with the baseline methods, DDQN reduces the waiting time for charging and the rejection rate by 70% and 53%, respectively. Taxi drives’ income are finally increased by about 7%. Moreover, the results of model parameter sensitivity analysis indicate that the charge speed and the number of vehicles have greater impact on drives’ income than the battery capacity. When the charging rate reaches 120 kW, electric taxis achieve the best performance. The government should build more fast charging station to improve the revenue of electric taxis.

Summary of PhD Thesis

Study on gravity inversion and seismogenic environment of density in Tibetan Plateau and its adjacent areas

LI Wei

2020, 49(12):  1640-1640.  doi:10.11947/j.AGCS.2020.20190475

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Estimation of forest above-ground biomass and net primary productivity using multi-source remote sensing data

LIU Yanan

2020, 49(12):  1641-1641.  doi:10.11947/j.AGCS.2020.20190443

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The research on multi-antenna GNSS/INS integrated position and attitude determination method

CAI Xiaobo

2020, 49(12):  1642-1642.  doi:10.11947/j.AGCS.2020.20190444

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Study of spaceborne GNSS-R for sea ice detection and sea ice concentration retrieval methods

ZHU Yongchao

2020, 49(12):  1643-1643.  doi:10.11947/j.AGCS.2020.20190473

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Research on forest key structural parameters estimation based on airborne LiDAR data

YOU Haotian

2020, 49(12):  1644-1644.  doi:10.11947/j.AGCS.2020.20190480

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