Table of Content

20 June 2023, Volume 52 Issue 6

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Geodesy and Navigation

Single station velocity determination of BDS-3 carrier phase observations with the constraints of heading angle

WANG Qianxin, HU Chao, WANG Zejie

2023, 52(6):  871-883.  doi:10.11947/j.AGCS.2023.20220355

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Velocity information is one of key state representations of the vehicle motion. The high-accuracy and reliable velocity based on BDS-3 observation is the requirements of BDS-3 high performance. To overcome impacts of error accumulation and observation environment on the traditional time-difference carrier phase (TDCP) model, a single station velocity determination of BDS-3 carrier phase observations with the constraints of heading angle is proposed. Firstly, the differential function of the undifferenced observation equation is constructed beside the traditional BDS-3 TDCP model, which is combined to simultaneously estimate the displacement increment of two adjacent epochs. Secondly, the correlation between displacement increments of horizontal plane and heading angle is used to construct the constraints of N and E directions. Thirdly, the combination of three groups of independent function equations is used to obtain the incremental displacement of vehicle epoch by epoch. According to static and kinematic experiments, it is indicated that an accuracy with mm/s level of velocity determination can be achieved in different directions under BDS-3 phase observations. Compared with the traditional TDCP method, the proposed method can improve the velocity accuracy of E and N directions with 62.9% and 87.5%, respectively, in which the accuracy of U direction is not significantly improved or even slightly decreased. Meanwhile, under the kinematic condition, it is suggested that BDS-3 phase velocimetry can obtain the accuracy of mm/s level in the horizontal plane of linear motion, in which E and N direction are 35.2% and 21.8% better than the traditional TDCP model. Furthermore, the accuracy of E, N and U directions in change direction is 2.81, 2.03, 1.91 cm/s, respectively, which is 45.9%, 41.2% and 56.2% higher than that of the traditional model. Therefore, the proposed single station phase velocity determination model considering heading angle constraints can effectively improve the velocity determination performance of BDS-3 satellite system.

A GNSS-R geometry computation method considering the Earth's curvature and ellipsoidal height

SONG Minfeng, HE Xiufeng, WANG Xiaolei, XIAO Ruya, JIA Dongzhen, LI Weiqiang

2023, 52(6):  884-894.  doi:10.11947/j.AGCS.2023.20210563

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Geometry computation is crucial for global navigation satellite systems reflectometry (GNSS-R), serving as a fundamental aspect for processing the reflected signals onboard and determining the measurement locations. However, the existing geometry computation methods can not meet the requirements of various scenarios, including land, ocean, and cryosphere applications, as this technique expands into new domains. This paper proposes a geometry computation strategy that achieves high accuracy and incorporates considerations for the Earth's curvature and ellipsoidal height. It integrates a initialization model for specular point calculation, and the accuracy of the initial estimation error can be reduced to within 5 km for different orbital altitudes (300~900 km) and geometric conditions. This method allows for high-precision geometry computation based on the WGS-84 ellipsoid, and takes into account the ellipsoidal height of the reflective surface, with an accuracy of less than 1 mm. The computational efficiency is significantly improved compared to existing methods, which can be beneficial for future demands of efficient computation considering surface height. Furthermore, the method can calculate the geometric path of the reflection signal by modifying the iterative equation, and achieves the integrated solution from the observed delay to the specular point and ellipsoidal height. Compared with the previous methods, it considers the Earth's curvature and the spatial offset of the reflection point varying with the ellipsoidal height, which can avoid the problem of inaccurate positioning of measurements in altimetric applications.

Residuals overbounding modeling of the tropospheric delay models

FU Yunri, YANG Ling, SHEN Yunzhong, LI Bofeng

2023, 52(6):  895-903.  doi:10.11947/j.AGCS.2023.20220231

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The satellite signals of the global navigation satellite system (GNSS) will be delayed when traveling through the troposphere, and these delays can be corrected by using empirical models. To ensure safe and reliable positioning, an overbounding model of tropospheric delay residuals after model correction should be established to monitor the integrity of positioning services. The radio technical committee for aeronautics (RTCA) recommended that the overbounding standard deviation of the zenith tropospheric delay used in aviation is 0.12 m. However, this constant value does not consider the geographical and seasonal variation of the tropospheric delay residuals, which would reduce the continuity and availability of GNSS in safety of life (SoL) applications. Considering the geographical and seasonal variations of the tropospheric delay residuals, the extreme value analysis is used to establish the overbounding model of the tropospheric delay residuals. Firstly, total 18-year residuals were standardized for each 10° latitude band to extract the seasonal variation. Then the general extreme value (GEV) distribution was fitted to the annual maximum and minimum values of the standardized residuals, and the extreme values at a probability of 10^-7 were calculated with the fitted parameters. Finally, the extreme values were converted to the overbounding standard deviation. The overbounding models of three tropospheric delay models, including GPT2w, UNB3 and Saastamoinen, are established by using this method, and the bounding performance of these models is validated by using the international GNSS service (IGS) tropospheric delay products. The residuals show that the overbounding model is conservative and available. For the UNB3 model, the calculated range of overbounding standard deviation is 0.05~0.12 m, which decreases by 4.1% to 57% compared with the constant value of 0.12 m specified by MOPS, effectively improving the continuity and availability of GNSS service in SoL application. For GPT2w and Saastamoinen models, the range of overbounding standard deviation is 0.03~0.08 m and 0.06~0.14 m, respectively.

Using FY-4A GIIRS data and ERA5 reanalysis data to build a regional atmospheric weighted mean temperature model in China

WANG Xinzhi, CHEN Fayuan

2023, 52(6):  904-916.  doi:10.11947/j.AGCS.2023.20210717

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The accuracy of atmospheric weighted mean temperature (T_m) directly affects the results of Global Navigation Satellite System (GNSS) precipitable water vapor inversion.For the existing T_m model parameters, modeling data sources to be optimized and the model construction only relies on a single sounding site or single grid point data. It is proposed to fuse FY-4A GIIRS data with ERA5 reanalysis data. A sliding window algorithm is introduced to process the fused data while taking into account the longitude, latitude and elevation factors to construct an empirical T_m model (FY-ET_m model) with a spatial resolution of 0.5°×0.5°. Bias and root mean square error (RMS) are used as accuracy metrics, combined with the 2020 sounding data, ERA5 reanalysis data, and zenith tropospheric delay (ZTD) products that are not involved in the modeling. Subsequently, the accuracy of the FY-ET_m model and its inverse precipitable water vapor (PWV) are evaluated. The results show that the average annual Bias and RMS of FY-ET_m model are -0.02、 5.79 K, respectively, which are 3.62(Bias)、 0.8(RMS)、 2.54(Bias) and 0.63 K (RMS) higher than those of Bevis and GPT3 models. With the reanalysis data of ERA5 as the reference value, the average annual Bias and RMS of FY-ET_m model are 0.01、 3.32 K, respectively, which are 0.97(Bias)、 0.13(RMS)、 2.94(Bias) and 1.71 K(RMS) higher than those of Bevis and GPT3 models. Compared with GPT3 model with excellent accuracy, FY-ET_m model also shows obvious accuracy improvement in western and northern China. With the PWV obtained from GNSS station as the reference value, the accuracy of PWV inversion from FY-ET_m model is similar to that from GNSS station, Bias ranges from -0.5 mm to 0.5 mm.The FY-ET_m model has high accuracy and good stability. It can obtain the T_mof the target point only by inputting the position and time information, and it can play an important role in GNSS precipitable water vapor inversion.

Monitoring glacier mass balance of the West Kunlun Mountains over the past 20 years by bistatic InSAR and ICESat-2 altimetry measurements

LI Tao, LI Chao, SHEN Xiang, JIANG Liming, WANG Hansheng, LI Gang, LIN Hui

2023, 52(6):  917-931.  doi:10.11947/j.AGCS.2023.20210213

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The ice mass balance of mountain glaciers is an important parameter of glacier changes research. Since the beginning of the 21st century, ice melting of most mountain glaciers has accelerated accompanying climate warming, but glaciers over the West Kunlun Mountains in the Qinghai-Tibet Plateau have shown a trend of stability and even an increase in ice mass, which is considered as the center of the “Karakoram anomaly”. However, there are still some debates about whether the ice mass changes in this region are still accumulated in recent years. Therefore, this paper uses the newly released ICESat-2 satellite altimetry and TanDEM-X 90m DEM data to quantitatively estimate the ice thickness changes and mass balance of the glaciers in the West Kunlun Mountains from 2013 to 2019. Moreover, the mass changes during the past 20 years, glacier surges and area changes were also analyzed with SRTM DEM, ice velocity data and Landsat images. The results show that: ① From 2013 to 2019, most of the glaciers over the West Kunlun Mountains still showed accumulation or stability, with the mass balance of 0.228±0.055 m w.e./a; ② Over the past 20 years, the glaciers have shown a positive trend in total, but the mass accumulation rate during 2013—2019 was higher than that in 2000—2013 (0.173±0.014 m w.e./a); ③ Glacier surges were still widely distributed after 2013. It was found that the 5Y641F0046 Glacier surged for the first time. The eastern branch of the West Kunlun Glacier and the 5Y641F0073 Glacier have been in an active phase for the past 20 years. The Zhongfeng Glacier changed from an active phase before 2013 to a quiescent phase and showed a trend of mass accumulation.

Photogrammetry and Remote Sensing

Hyperspectral anomaly detection combining sparse constraint and feature extraction via stacked autoencoder

SONG Shangzhen, YANG Yixin, WANG Huifeng, WANG Xiaoyan, RONG Shenghui, ZHOU Huixin

2023, 52(6):  932-943.  doi:10.11947/j.AGCS.2023.20210604

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Anomaly detection of hyperspectral images has important application value in military, agriculture, exploration, fire protection and other fields. Traditional algorithms of hyperspectral image (HSI) anomaly detection (AD) do not effectively mine the deep features of the image spectrum, while the deep learning method has good ability to extract deep feature information. Since the AD problem generally cannot obtain the prior information in advance, the unsupervised network is more suitable. Existing AD algorithms based on autoencoder (AE) does not make effective use of the local information, resulting in limited detection effect. To overcome this shortcoming, the paper proposes an AD method based on sparse representation (SR) constraints for stacked autoencoder (SAE). Firstly, the semantic information is obtained by SAE. Secondly, the SR is used as a constraint to effectively combine with the encoder, and the local characteristics of the feature elements in the potential hidden space are mined. Finally, the fractional Fourier transform is utilized, and the characteristics of the original spectrum and its intermediate domain of Fourier transform are obtained by spatial-frequency representation. Consequently, the spectral discrimination between background and anomalies is further enhanced, and the effect of noise is also removed. The experiment performs verification on 5 HSIs collected by 4 spectrometers including Hymap, AVIRIS, ROSIS, and HYDICE. The area under curve (AUC) values are 0.990 5, 0.998 3, 0.999 0, 0.992 8 and 0.911 0, respectively. Compared with compared algorithms, the effect of the proposed algorithm can be improved.

Semi-empirical waveform decomposition method for correction of near water surface penetration error in airborne LiDAR bathymetry

WANG Dandi, XU Qing, XING Shuai, LIN Yuzhun, ZHANG Guoping

2023, 52(6):  944-955.  doi:10.11947/j.AGCS.2023.20210603

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The accuracy of signal detection is a key factor that affects the final measurement results of airborne LiDAR bathymetry. To solve the problem that green laser penetrates the water column in near water surface and improve the accuracy of the detected water surface signal, a semi-empirical waveform decomposition method is proposed. A semi-empirical signal convolution model that conforms to the field waveforms is constructed by simplifying the laser radiation transmission model. Deep water waveform samples are manually collected using the flight trajectory and image to estimate the initial values and ranges of the water column parameters in the model. Based on the trust region algorithm, each component of the waveform is precisely reconstructed with the constraints of the waveform priors, so the position of the water surface signal is obtained, and the near water surface penetration error is corrected. The experimental results show that the proposed method combines theory and experience in waveform decomposition, adapting to waveforms with different water depths and improving the accuracy of the detected water surface signal with good waveform fitting. Compared with the deconvolution algorithm and the traditional waveform decomposition method, the accuracy of the proposed method achieves 44% and 51% improvement, respectively.

Point cloud virtual datum determination method in deformation analysis

SUN Wenxiao, WANG Jian, JIN Fengxiang, YANG Yikun

2023, 52(6):  956-965.  doi:10.11947/j.AGCS.2023.20210463

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Aiming at the problem that conventional fixed-point-based datum construction methods are difficult to apply to overall datum detection based on point cloud, a point cloud virtual datum determination method based on the centroid stability and distribution similarity of corresponding grids of the multitemporal laser point cloud is proposed in our study. Firstly, the point cloud distribution characteristics in the stable and deformed areas are analyzed according to the changing pattern of the traditional datum points, and the detection principle of the point cloud virtual datum is discussed. Then, the point position errors caused by distance measurement, angle measurement, and footprint scale are weighted, and the axis vectors angle corresponding to the minimum moment of inertia is calculated to determine the point cloud distribution similarity. Furthermore, the centroid stability is analyzed by using the squared M_splitsimilarity transformation, and the virtual datum of the multitemporal point cloud is detected. Finally,the tank, landslide, and regular geometry point cloud captured by terrestrial laser scanning technology are applied to verify the feasibility and application scehes of the proposed method. Results show that the point cloud virtual datum that does not require on-site layout or regular maintenance can be detected by combining the centroid stability and the distribution similarity characterized by the axis vectors angles of the minimum moment of inertia, which provides the foundation for multitemporal point cloud coordinate unification and deformation analysis.

A hybrid SfM method considering scene connectivity

QU Wenhu, LIU Zhendong, CAI Haolin, ZHANG Shaizhe

2023, 52(6):  966-979.  doi:10.11947/j.AGCS.2023.20220448

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SfM method has achieved great success in 3D sparse reconstruction, but it still meets serious challenges in large-scale scene reconstruction. Aiming at solving the problem of loose image distribution, low efficiency of subcluster expansion and weak robustness of subcluster merging in existing hybrid SfM methods, a hybrid SfM method considering scene connectivity isproposed in this paper. Firstly, a multifactor joint scene division algorithm based on normalized cut is proposed, which effectively solves the problem of loose image space distribution in subclusters after scene division; Secondly, a subcluster balanced expansion algorithm considering partition connectivity is proposed to improve the expansion efficiency and connectivity between subclusters; Then, a quality check and secondary reconstruction mechanism in the local reconstruction stage are introduced to eliminate the influence of subclusters with unqualified local reconstruction quality on merging, and a subcluster merging algorithm considering the connectivity between clusters is proposed to implement the robust merging among subclusters. Finally, experimental validation is conducted using multiple open datasets and multi-view datasets, and the results show that the method proposed in this paper is superior to state-of-the-art methods in terms of robustness and efficiency, and has better feasibility and advancement.

Improved U-Net remote sensing image semantic segmentation method

HU Gongming, YANG Chuncheng, XU Li, SHANG Haibin, WANG Zefan, QIN Zhilong

2023, 52(6):  980-989.  doi:10.11947/j.AGCS.2023.20210684

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Semantic segmentation of remote sensing images by deep neural network is an important content of remote sensing intelligent interpretation, which plays a very important role in urban planning, disaster assessment, agricultural production and other fields. High resolution remote sensing images are characterized by complex background, diverse scales and irregular shape, etc. Therefore, using natural scene semantic segmentation methods to process remote sensing images often has the problem of low segmentation accuracy. Based on the U-Net model, a multi-scale skip connection method is proposed to integrate semantic features of different levels and obtain accurate segmentation boundary and location information. Attention mechanism and pyramid pooling are introduced to solve the problem of fine segmentation in complex background. In order to verify the effectiveness of our proposed method, experiments were carried out on the WHDLD and LandCover.ai dataset and compared with the mainstream semantic segmentation methods. The experimental results show that the proposed method outperforms other comparison methods, with mIoU reaching 74.28% and 82.04% respectively, and with average of F₁ score reaching 84.47% and 89.76% respectively; compared with the segmentation results of U-Net, the value of IoU improves significantly in buildings, roads and other categories with a relatively small proportion, and is superior to other comparison methods.

Line matching algorithm for cross-view images combining neural network learning with grayscale information

SONG Jiaxuan, FAN Dazhao, DONG Yang, JI Song, LI Dongzi

2023, 52(6):  990-999.  doi:10.11947/j.AGCS.2023.20220468

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The geometric deformation of cross-view images is large, and the unilateral neighborhoods of corresponding lines are prone to significant differences, making it difficult for traditional line matching algorithms to obtain reliable line pairs. To solve this problem, a line matching algorithm for cross-view images combining neural network learning with grayscale information is proposed. Firstly, the pixel-level directional gradient histogram features of the images are obtained and a neural network is applied to fuse these features and the image greyscale information to form a feature description grid. Then, the discrete points are extracted from the line, and the one-sided descriptions of the points are calculated according to the information of the discrete points' unilateral features description grid. The one-sided descriptors of the points are aggregated by the deep learning method to form the unilateral abstract expression of the line. Finally, the known corresponding points are used to constrain the matching region for group matching, and the matching results are verified by comparing the topological consistency of the line pairs to obtain the final matching line pairs. A number of representative groups of public cross-view images from different sources are selected for line matching experiments and they are compared with mainstream line matching algorithms for analysis. The results show that the proposed algorithm is able to obtain uniformly distributed line pairs with high correct rates for cross-view images from different sources with obvious differences in content, and realize robust line matching of cross-view images from different sources.

Cartography and Geoinformation

Extracting road intersections from vehicle trajectory data in the face of trace density disparity

DENG Min, LUO Bin, TANG Jianbo, YAO Zhipeng, LIU Guoping, WEN Xiang, HU Runbo, CHAI Hua, HU Wenke

2023, 52(6):  1000-1009.  doi:10.11947/j.AGCS.2023.20210173

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Vehicle trajectory data provides a new opportunity for road network generation, road map update and traffic condition monitoring. Accurately extracting road intersections from trajectory data is a key step to build a refined road network map based on vehicle trajectory data. At present, several scholars have put forward some methods using spatial clustering to identify road intersections based on the detection of turning points and speed change positions in trajectories. However, due to the heterogeneity of track density distribution, noise interference and the issue of clustering parameters setting, the existing methods still have challenges to extract intersections of different sizes and shapes from low-quality trajectory data. Therefore, this paper puts forward a strategy of track rasterization considering the heterogeneity of track density distribution and a road intersection extraction method based on the trajectory transformation, intersection segmentation and location optimization process. Experiments on real-world trajectory data with different sampling frequencies are conducted to evaluate the performance of the proposed method, and results show the effectiveness and superiority of the proposed method over the existing state-of-the art methods.

TIN-DDM terrain complexity measurement method considering topographic forms differences

JI Hongchao, DONG Jian, LI Shujun, ZHANG Zhiqiang, TANG Lulu

2023, 52(6):  1010-1021.  doi:10.11947/j.AGCS.2023.20220074

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Terrain complexity is a comprehensive parameter in the research field of seabed topographic forms, and reasonable construction of TCI calculation model is one of the important links to measure the changes of DDM surface topography. At present, the TCI calculation models established by the TIN-DDM terrain complexity measurement method cannot well reflect the difference in the topographical change characteristics between different terrain units where they are located by comparing the TCI values of the sampling points, and aiming at this problem, this paper proposes a TIN-DDM terrain complexity measurement method fully considering topographic forms differences. On the basis of analyzing the principle of rolling ball transformation, this method clarifies the correlation between the depth change of the sampling point and its TCI in the process of TIN-DDM rolling ball transformation, analyzes the selection principle of the optimal rolling ball radius, constructs the sampling point TCI calculation model and optimized it. Finally, an effective measure of the terrain complexity of TIN-DDM is realized by means of TCI interpolation of complex fields. The experimental results show that this method has a good effect of measuring terrain complexity, the constructed TCI calculation model can better distinguish the difference of local terrain topography expressed by sampling points. Compared with the comparison method, the DDM reconstructed by TCI according to the method in this paper has higher accuracy.

Automatic extraction method of depth annotation in grid chart considering correct classification and accurate positioning of elements

MA Mengkai, DONG Jian, TANG Lulu, PENG Rencan, ZHOU Yinfei, WANG Fang

2023, 52(6):  1022-1036.  doi:10.11947/j.AGCS.2023.20220258

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Aiming at the problems of difficult implementation, low accuracy and low efficiency of automatic extraction of depth annotation, CNN model is applied to automatic recognition of depth annotations. Combined with the spatial distribution and geometric characteristics of depth annotation, the traditional pattern recognition algorithm is improved, a grid chart depth annotation automatic extraction method considering the correct classification and accurate positioning of elements is proposed. Through the quantitative expansion of the neighborhood of chart slices, an adaptive chart segmentation model considering the integrity of elements is established, which overcomes the limitations of CNN model applied to large format chart element recognition. Combined with the analysis and evaluation of the spatial relationship of the corner position of the prediction frame, the principle of determining the uniqueness of elements facing the airspace conflict is designed, and the problem of repeated recognition of depth annotation caused by neighborhood expansion is solved. On this basis, the spatial distribution law of the location of the main points of depth annotation is further demonstrated, and an improved connected domain analysis model considering the geometric distribution characteristics of elements is established, which realizes the accurate positioning and numerical extraction of depth annotation. The experimental results show that: ① This method can achieve the automatic extraction of depth annotation, and has high recall and precision in the process of classification and rough positioning of depth annotation based on CNN model. At the same time, the accuracy of the final depth annotation value extraction is high, and the position of the main point can meet the special requirements of depth annotation extraction; ② Through the comparative experiments of various CNN models applied in the automatic extraction model in this paper, the effectiveness of different CNN models in the automatic extraction model in this paper is compared, and the suggestions for the adoption of CNN models are analyzed and given. At the same time, the CNN model with good performance is selected as the CNN model used in this model and compared with the traditional pattern recognition method. According to the processing time and the accuracy recall results show that this method has high recognition accuracy and efficiency.

Summary of PhD Thesis

Research on accuracy evaluation and model establishment of ZTD/PWV based on GNSS and reanalysis

WANG Shuaimin

2023, 52(6):  1037-1037.  doi:10.11947/j.AGCS.2023.20210594

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Research on ionosphere inversion and its applications based on SAR techniques

LI Bing

2023, 52(6):  1038-1038.  doi:10.11947/j.AGCS.2023.20210598

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Research on user check-in characteristic analysis and recommendation methods in location-based social networks

ZHANG Zhiran

2023, 52(6):  1039-1039.  doi:10.11947/j.AGCS.2023.20210605

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Study of differential code bias estimation and ionosphere modeling using multi-mode and multi-frequency GNSS observations

WANG Qisheng

2023, 52(6):  1040-1040.  doi:10.11947/j.AGCS.2023.20210624

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Extraction of buildings along high-speed railway from high resolution remote sensing images based on convolutional neural network

WANG Jicheng

2023, 52(6):  1041-1041.  doi:10.11947/j.AGCS.2023.20210633

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Research on robust structure from motion via optimizing view-graph

XIAO Teng

2023, 52(6):  1042-1042.  doi:10.11947/j.AGCS.2023.20210673

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Mars spacecraft precise orbit determination and phobos gravity field recovery

YANG Xuan

2023, 52(6):  1043-1043.  doi:10.11947/j.AGCS.2023.20210692

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Extraction and spatiotemporal variation of basal melting channel under ice shelf using multi-sources data

SONG Xiangyu

2023, 52(6):  1044-1044.  doi:10.11947/j.AGCS.2023.20210694

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