Table of Content

    20 March 2024, Volume 53 Issue 3
    The progress and trend of cartography and geographic information engineering in China (2019—2023)
    SUN Qun, REN Fu, LONG Yi, CHEN Xin, XU Gencai, LIU Jianjun, ZHOU Zhao
    2024, 53(3):  399-412.  doi:10.11947/j.AGCS.2024.20230562
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    Based on the development of cartography and geographic information technology in China since 2019, this report summarizes the achievement in theories of cartography, digital cartography and press techniques, updating of China's National Fundamental Geographic Information Database, geographic information technology and industrial development, geographic information applications and services, atlas compiling and publishing, and the new online map. Finally, the report offers some prospects for the developing trend of cartography and geographic information engineering in China.
    Geodesy and Navigation
    GNSS ultra-rapid orbit and clock offset estimation method with the aid of the constraint of BDS-3 onboard clock
    HU Chao, WANG Qianxin
    2024, 53(3):  413-424.  doi:10.11947/j.AGCS.2024.20230168
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    The high-stability BDS-3 onboard clock is one of the significant advanced technologies of BDS, which is underutilized in GNSS data processing. To solve the precision restriction of GNSS ultra-rapid orbit and clock offset parameters under the strict timeliness limitation, GNSS ultra-rapid orbit and clock offset estimation method with the aid of the constraint of BDS-3 onboard clock is proposed in this research. Firstly, based on the correlation GNSS clock offset and orbit, the GNSS orbit determination model is constructed with the consideration of BDS-3 onboard clock parameter. Secondly, impacts of onboard clock constraints on GNSS orbit determination are analyzed by taking BDS-3 precise clock offsets as example in experiments. Thirdly, to overcome the influence of the prediction clock offset and selected constraints on GNSS ultra-rapid orbit determination, the synchronously processed method of BDS-3 clock modelling and GNSS ultra-rapid orbit estimation is proposed. According to the experimental results, it is indicated that the accuracy of BDS-3 and GPS orbit and clock offsets can be respectively improved with 27.5%, 5.1% and 20.2%, 5.2%, under the optimal constraint on BDS-3 onboard clock. Meanwhile, compared with the traditional epoch-wise white noise strategy of GNSS clock parameter, the proposed one-step processing method for BDS-3 satellite clock modeling and GNSS ultra-rapid orbit determination can respectively improve the accuracy of GNSS ultra-rapid clock offset and orbit up to 4.8% and 34.2%, where the millimeter-level orbit accuracy improvements can be obtained. Therefore, the proposed GNSS ultra-rapid orbit and clock offset estimation method based on BDS-3 clock offset constraints can effectively utilize the information of BDS-3 highly stable onboard clock information, and realize the accuracy improvement of GNSS ultra-rapid orbit and clock offsets.
    A distributed GNSS/SINS/odometer resilient fusion navigation method for land vehicle
    MU Mengxue, ZHAO Long
    2024, 53(3):  425-434.  doi:10.11947/j.AGCS.2024.20220349
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    To improve the fault-tolerance of a low-cost land vehicle navigation system in the complex environment, this paper proposes a distributed GNSS/SINS/odometer resilient fusion method based on the suboptimal gain fusion algorithm. First, a velocity compensation model for each odometer on four wheels is established according to the Ackermann steering geometry, which improves the accuracy of forward and lateral velocity measurement at the inertial measurement unit center. Then, a fault detection and classification criteria based on Chi-square test statistics is designed to make full use of the available observation information. Last, a resilient adjustment model for the stochastic model and information sharing factors (ISF) are proposed to mitigate the influence of abnormal observation from the sensor layer and the decision layer respectively and realize the resilient fusion of multi-source information. A real car test is carried out to verify the effectiveness of the distributed GNSS/SINS/odometer resilient fusion method. The experiment results demonstrate that the proposed method can effectively reduce the impact of subsystem faults on the global state estimation and improve the fault tolerance performance of the system in complex environments. Moreover, compared with the traditional federated Kalman filtering (FKF), the SGF algorithm can achieve the equivalent accuracy with significant computational efficiency improvement, which is conducive to the practical engineering application of multi-source information resilient fusion.
    An InSAR phase unwrapping method based on R2AU-Net
    HE Yi, YANG Wang, ZHU Qing
    2024, 53(3):  435-449.  doi:10.11947/j.AGCS.2024.20230305
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    The accuracy of terrain elevation or surface deformation retrieval relies heavily on the quality of InSAR phase unwrapping. Conventional phase unwrapping techniques, rooted in non-machine learning models (such as path-following or minimum norm), face challenges in producing accurate unwrapping outcomes within areas of low coherence or high phase gradients (dense interference fringes). Deep neural network models offer distinct advantages in nonlinear representation and feature expression, widely employed in digital image processing research, wherein InSAR phase unwrapping parallels image regression.This paper presents an InSAR phase unwrapping approach utilizing the R2AU-net. Initially, pairs of wrapped and unwrapped phases are simulated through mathematical fractal methods, circumventing inherent errors and artifacts introduced by integrating external DEMs into the phase. This approach maintains terrain feature diversity and complexity while providing the requisite dataset for model training. Subsequently, the R2AU-net phase unwrapping model, built upon the foundational U-net model, incorporates attention mechanisms to augment the model's convolutional feature selection capacity, thereby improving unwrapping performance in regions of low coherence or dense striping. The utilization of recurrent residual convolutional structures addresses the vanishing gradient issue, enhancing the model's feature representation capability.Ultimately, experimental analyses are conducted using both simulated and real data. The results demonstrate that the proposed R2AU-net phase unwrapping model effectively retains terrain elevation or real surface deformation information, thereby bolstering the reliability of unwrapping outcomes. In terms of performance, it surpasses established methods such as the Goldstein branch-cut method, SNAPHU method, as well as CNN and U-Net phase unwrapping models.
    PolSAR image registration combining polarization whitening filtering and SimSD-CapsuleNet
    XIANG Deliang, DING Huaiyue, GUAN Dongdong, CHENG Jianda, SUN Xiaokun
    2024, 53(3):  450-462.  doi:10.11947/j.AGCS.2024.20230197
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    Polarimetric synthetic aperture radar (PolSAR) image registration has a wide range of applications in feature classification, change detection, and image fusion. Existing PolSAR image registration methods, whether based on deep learning or conventional methods, use PolSAR magnitude image information for processing. This processing leads to a large amount of polarization information loss, and at the same time, the registration accuracy and reliability perform poorly under the influence of the inherent coherent speckle noise of PolSAR images. To this end, this paper first develops a novel and effective key point detector based on polarization whitening filter (PWF) refinement processing, which uses PWF to suppress coherent speckle noise in PolSAR images and selects significant and uniformly distributed matching key points by threshold constraint, morphological erosion, and non-extreme value suppression. Further, in this paper, we design a Siamese simple dense capsule network (SimSD-CapsuleNet) to quickly extract the shallow texture features and deep semantic features of the data, and we use the polarization covariance matrix as the input data in order to make full use of the polarization information. In this paper, the distances between the capsule form feature descriptors are calculated and fed into a hard L2 loss function for the training of the model. The method in this paper is validated on PolSAR images acquired by different sensors with different resolutions, and the results show that the method can acquire more uniform and a larger number of matching key points in a shorter time, and the combination of PWF and deep neural network can achieve fast and accurate PolSAR image registration.
    Analysis of interferometric mapping accuracy for spaceborne distributed SAR dual-frequency alternative bistatic mode
    WANG Yuan, XU Huaping, LI Chunsheng, ZENG Guobing, LIU Aifang, GE Shiqi
    2024, 53(3):  463-472.  doi:10.11947/j.AGCS.2024.20220657
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    Due to flexible baseline and minimal temporal decorrelation, spaceborne distributed synthetic aperture radar (SAR) systems have been rapidly developed and widely applied in interferometric mapping. Dual-frequency alternative bistatic mode is an emerging mode, it can simultaneously acquire multiple SAR images at two frequencies compared with the conventional mode, which enables higher accuracy of interferometric mapping. In this paper, the fundamental of dual-frequency alternative bistatic mode is briefly introduced. Then, the phase ratio relationship and coherence characteristics of SAR interferometric pairs in this mode are investigated in depth, and the interferometric phase accuracy and mapping accuracy are analyzed theoretically, which provides a theoretical basis for the design of the SAR systems and the study of the interferometric processing. Finally, simulation results demonstrate the superiority of the dual-frequency alternative bistatic mode over the conventional mode. This mode can increase the interferometric phase data of different frequencies, thereby improving the accuracy of interferometric mapping.
    A multi-baseline phase unwrapping method based on a discrete optimization framework
    YUE Jiawei, HUANG Qihuan, LIU Hui, MA Zhangfeng
    2024, 53(3):  473-481.  doi:10.11947/j.AGCS.2024.20220617
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    Multi-baseline phase unwrapping breaks through the limit of phase continuity assumption through extending the ambiguity boundary of single-baseline phase unwrapping. However, phase noise is still challenging the multi-baseline unwrapping. The clustering analysis algorithm can suppress the noise to a certain extent, but it is hard to guarantee continuity of cluster edges. In this paper, a discrete-optimization-based multi-baseline InSAR phase unwrapping algorithm is proposed, which transforms the classical multi-baseline unwrapping into a discrete optimization problem and constructs a multi-baseline unwrapping analytical framework. The method solves the phase ambiguity in the bidirectional form, and introduces block clustering to correct the abrupt change of the phase ambiguities caused by heavy noise, improving the robustness of the algorithm and overcoming the cluster boundary hopping. The effectiveness of the method has been validated through simulation and real data tests. The results show that the proposed algorithm reduces the root mean square error by about 20% compared with the traditional clustering method.
    GNSS-IR retrieval method with consideration of tidal periodicity of sea level
    WANG Xiaolei, NAN Yang, HE Xiufeng, SONG Minfeng
    2024, 53(3):  482-492.  doi:10.11947/j.AGCS.2024.20220659
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    Sea level monitoring is important for coastal production and safety, marine monitoring and analysis. With the development of Global navigation satellite systems (GNSS), a new technology called GNSS-interferometry reflectometry (GNSS-IR) has been demonstrated can be used to monitor coastal sea level. The vertical distance between reflecting surface and antenna named reflector height (RH) can be estimated from the observations, and then the sea level can be retrieved. There is an important error named height variation error in this technique causing by the movement of sea surface. However, the classical correction method cannot calculate RH rate accurate and subsequently cannot effectively correct the error. So, this paper presented a GNSS-IR combination method with the consideration of tidal periodicity of sea level. Based on the tidal wave coefficient, the RH rate of change can be predicted and incorporated into the GNSS-IR combination equation to better correct the height variation error. The paper conducted experiments using three international GNSS stations. Compared with sea level measurements, the results showed that the accuracy of the GNSS-IR combined retrievals were improved by approximately 1.2cm when tidal wave characteristics were considered than when were not, and they achieved an accuracy improvement of 20% to 70% compared to classical algorithms. The results indicate that the improved algorithm corrects height variation errors more effectively by predicting RH rate of change at different times through tidal analysis.
    Intelligent identification of bottom layer boundaries in shallow sections of ocean waterways using improved region growth algorithm
    JIANG Tingchen, MENG Haofan, WANG Xiao, WANG Chaojin, YANG Yi, NING Yaoyao, YAN Yuru
    2024, 53(3):  493-502.  doi:10.11947/j.AGCS.2024.20220677
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    It is of great significance to seabed exploration for the development and utilization of marine resources, marine engineering construction, and national defense security. As an acoustic device capable of surveying the distribution of bottom sediment in the shallow surface of the seabed, the accuracy of bottom sediment identification currently depends on the subjectivity of the operator, with poor reliability. In order to improve efficiency and interpretation accuracy, it is necessary to further study the intelligent identification model for the bottom layer boundary. In the paper, an improved region growth algorithm suitable for seabed bottom layer boundary recognition without human intervention is proposed. That is, based on the study of grayscale mapping and noise elimination, the skeleton information of the image layer boundary is extracted using an iterative maximum class difference algorithm, and then the skeleton information is used as an initial growth point and the growth direction is corrected using rheological properties. At the same time, the algorithm combines grayscale weighted mapping curves and peak valley wavelength constrained growth neighborhoods to segment layer boundaries, extract edges, and connect them into lines, thereby seabed bottom layer boundary recognition is ultimately achieved. The experimental results of shallow section survey data of Lianyungang port waterway show that the improved region growth algorithm can effectively identify the boundary of the bottom layer, and its recognition accuracy reaches centimeter level, which can meet the requirements of seabed sediment interpretation and analysis.
    hotogrammetry and Remote Sensing
    The positioning method of lunar observations by Jilin-1 Guangpu satellite
    JING Zhenhua, HU Xiuqing, LI Shuang
    2024, 53(3):  503-511.  doi:10.11947/j.AGCS.2024.20220597
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    The clarification of pixel positions in remote sensing images is an essential prerequisite for data applications, and in combination with the high-resolution Moon views obtained by Earth-orbiting satellites, it is promising to calibrate satellite instruments directly by the radiance of specific regions of the lunar surface. This paper proposes a method to calculate the lunar image coordinates without relyingstrictly on the instrument parameters for the Jilin-1 GuangPu02 (JL1GP02) lunar observations. The selenographic coordinates of the two-dimensional image pixels are calculated by the satellite view libration at the observation moment and the orientation relationship between the Moon and instrument scans, and the geometric positioning quality is evaluated by creating simulated images. Results show that the geometric deviations of the lunar observation images at different resolutions are usually within 1.3 pixels, and the time series results for about two years agree well and generally follow a normal distribution, indicating the reliability of the positioning results. The method can avoid complicated coordinate transformation and achieve image-level geometric positioning of lunar images when the interior orientation parameters are unknown.
    A self-supervised pre-training scheme for multi-source heterogeneous remote sensing image land cover classification
    XUE Zhixiang, YU Xuchu, LIU Jingzheng, YANG Guopeng, LIU Bing, YU Anzhu, ZHOU Jianan, JIN Shanghong
    2024, 53(3):  512-525.  doi:10.11947/j.AGCS.2024.20220553
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    Deep learning has revolutionized the remote sensing image processing techniques over the past few years. Nevertheless, it is laborious to annotate high quality samples, thus limiting the performance of deep networks because of insufficient supervision information. To resolve this contradiction, we investigate the self-supervised pre-training and fine-tuning paradigm for multi-source heterogeneous remote sensing image land cover classification, aiming to relieve the urgent need for manually annotated data. Specifically, the proposed generative feature learning model consists of asymmetric encoder-decoder structure, in which the deep encoder extracts high-level key characteristics contained in multi-source data and task-specific lightweight decoders are developed to reconstruct original data. To further improve the feature representation capability, the cross-attention layers are utilized to exchange information contained in heterogeneous characteristics, thus learning more complementary information from multi-source remote sensing data. In fine-tuning stage, the trained encoder is employed as unsupervised feature extractor, and learned features are utilized for land cover classification through the designed lightweight Transformer based classifier. This self-supervised pre-training architecture is capable of learning high-level key features from multi-source heterogenous remote sensing images, and this process does not require any labeled information, thus relieving the urgent need for labeled samples. Compared with existing classification paradigms, the proposed multimodal self-supervised pre-training and fine-tuning scheme achieves superior performance for remote sensing image classification.
    Log-Gabor filter-based high-precision multi-modal remote sensing image matching
    CAO Fanzhi, SHI Tianxin, HAN Kaiyang, WANG Pu, AN Wei
    2024, 53(3):  526-536.  doi:10.11947/j.AGCS.2024.20230059
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    A feature matching method based on Log-Gabor filtering is proposed to address the problem of high-precision matching for multimodal remote sensing images. The method adopts a multi-scale dense matching framework via a coarse-to-fine manner, which avoids the low repeatability problem of feature detectors in multimodal images and is able to extract a large number of accurate correspondences. The method consists of two main steps: first, a feature pyramid robust to non-linear radiometric differences between images is constructed using multi-scale multi-angle Log-Gabor filters; then, the coarse feature map is used for dense template matching to extract a large number of coarse feature correspondences; the feature pyramid is then used to achieve bottom-up refinement of coarse correspondences layer by layer. Furthermore, to address the problem of inefficient sliding window operation for template matching, a fast implementation method of dense template matching is proposed, which effectively reduces the running time of dense template matching. The results show that the proposed method can overcome the influence of non-linear radiation differences between images, and outperforms existing multimodal image feature matching methods in terms of the number of correct matches, matching accuracy and matching precision.
    Improved real-time intelligent recommendation method of remote sensing information in deep cross network
    PENG Ranshu, CHEN Shi, CHEN Yu
    2024, 53(3):  537-547.  doi:10.11947/j.AGCS.2024.20230065
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    With the advent of the era of remote sensing big data, the problem of active and real-time push of massive remote sensing data has become a bottleneck limiting the development of remote sensing information intelligent services. Aiming at the problems of insufficient spatial feature expression ability, insufficient cross feature expression ability and non-discriminatory treatment of cross features in existing remote sensing information recommendation models, this paper proposes an attention deep cross spatial transformation network (ADCSTN) integrating attention mechanism. Firstly, the model uses deep cross network to extract the cross features of different associations of remote sensing information. Then, based on grid division, the model uses the spatial transformation layer to convert the one-dimensional spatial attribute data into two-dimensional spatial matrix, fully capturing the spatial structure characteristics of remote sensing information. Finally, the attention layer sets different weights for the different associated cross features to enhance the performance of the model and realize the active, real-time and intelligent push of remote sensing information. In this paper, the remote sensing satellite constellation composed of 1584 intelligent remote sensing satellites is simulated by STK to provide real-time remote sensing data for ships in the area of 20°N—40°N, 120°E—140°E, and set user interests to obtain the experimental data set. The experimental results show that the recommendation effect of the model in this paper is better. Compared with the traditional quad model, the F1 score is increased by about 50%.
    Solving camera pose with multiplexing mirror constraint
    A Xiaohui, LI Jiatian, LIU Jiayin, HU Hao, HE Rixing, LU Mei, DUAN Ye
    2024, 53(3):  548-557.  doi:10.11947/j.AGCS.2024.20220362
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    Aiming at the problem that the reference object is not in the field of view, a method with constraints of mirror camera to solve camera pose is proposed.The steps are:① Mirror camera pose is solved by using the method of camera pose estimation. ② The linear constraint relationship between the pose of camera and single image mirror camera is constructed with mirror reflection symmetry. ③ To establish constraints between multiple images, the mirror camera poses were multiplexed, 18 linear constraints can be obtained using 3 images. This makes the accuracy of camera pose solution improved. Experiments show that:the errors of rotation matrix and translation vector are 0.009° and 1.866mm respectively, and the corresponding errors are reduced by 0.009°and 2.292mm on average, and has a strong adaptability. In real experiments, the average reprojection error is 0.492 pixels. This suggests that the method in this paper is better than the comparison methods, and the pose of the camera where the reference object is not in the field of view can be accurately solved.
    Intelligent detection method of image water level inversion for water level without water scale in complex scenes
    SUN Chuanmeng, WEI Yu, LI Xinyu, MA Tiehua, WU Zhibo
    2024, 53(3):  558-568.  doi:10.11947/j.AGCS.2024.20220561
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    Realizing fine water control and flood warning requires real-time and accurate perception of sudden water level change events. The existing water level recognition technology cannot meet the needs of water level recognition in complex and harsh environments such as night, haze, rain, snow, floating object occlusion and shadow. To this end, this paper proposed an intelligent water level detection technique without water scale by integrating improved YOLOv5 and Kalman filtering principles: ① Introducing YOLOv5 to detect the water level line (water shore demarcation line) and using linear fitting methods to obtain the actual water level line; ② The water level is infinitely large in the extension direction and infinitely small in its normal direction., a multi-level feature fusion method was proposed to strengthen the mesoscale features to improve the original YOLOv5 algorithm; ③ Using Kalman filtering to introduce water level history information as a priori knowledge to improve the generalization performance of this technique to complex and harsh environments; ④ Adding a fixed marker pre-calibrated in the image to the deep learning network for training, and solving the actual water level elevation based on the real size of the marker to achieve a water-rule-free detection scheme. Relevant experiments and practice showed that the improved YOLOv5 was more lightweight; the slope accuracy of the water level intelligent detection method described in this paper was 97.3%, which was 2.4% higher than the original model; the intercept accuracy was 99.3%, which was 0.5% higher than the original model; the water level elevation could be automatically and accurately identified in complex and harsh environments such as night, haze, rain, snow, floating object occlusion, shadow, and the error was less than 0.1m.
    Cartography and Geoinformation
    A local encryption method for vector maps based on multilevel spatial index structure
    DING Chen, PENG Cheng, TANG Jianbo, DENG Min, YANG Xuexi, LIU Huimin
    2024, 53(3):  569-581.  doi:10.11947/j.AGCS.2024.20220564
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    Vector maps are the basic data sources of location-based services. In the big data era, vector maps have been used in most of our daily life service applications. The development of big data technologies, which provide great convenience for sending, sharing, acquisition and dynamic update of vector maps, also make it is severe of the security risks such as information leakage and stealing in the process of map data transmission. Existing map encryption methods are usually based on traditional cryptographic algorithms and encrypt the entire vector map, without making full use of the spatial data structure and the distribution characteristics of geographical entities in maps, which make it is difficult for these methods to encrypt parts of the data and cannot meet the needs of applications such as crowdsourced data collection, personalized map services for encrypting parts of the data rather than the entire data. Therefore, this paper proposes a local encryption method for vector maps based on multilevel spatial index structure. Experimental results on real point, line and polygon vector maps show that: ① The proposed method can effectively perform local encryption and local decryption for vector maps; ② Compared with the state-of-the art encryption methods, the proposed method has higher encryption efficiency; ③ The security and the anti-attack ability of the proposed method are significantly improved by using different encryption parameters for different encryption units, which ensures the security of the encryption results.
    Summary of PhD Thesis
    Study on technologies of moving targets localization using binocular stereo vision in haze environment
    DING Jing
    2024, 53(3):  582-582.  doi:10.11947/j.AGCS.2024.20230014
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    Research on methods of three dimensional displacement field extraction based on GNSS and InSAR observations
    XIONG Luyun
    2024, 53(3):  583-583.  doi:10.11947/j.AGCS.2024.20230033
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    Research on the method of maintenance of regional reference frame based on GNSS
    JIANG Guangwei
    2024, 53(3):  584-584.  doi:10.11947/j.AGCS.2024.20230052
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    Research on multi-view image photometric and geometry registration algorithm
    YANG Junxing
    2024, 53(3):  585-585.  doi:10.11947/j.AGCS.2024.20230057
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    Research on key technologies of point cloud registration and 3D object recognition using local shape description
    TAO Wuyong
    2024, 53(3):  586-586.  doi:10.11947/j.AGCS.2024.20230058
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    Modeling and analysis of regional geoid anomalies combining with surface mass load and GRACE/GRACE-FO data
    XU Pengfei
    2024, 53(3):  587-587.  doi:10.11947/j.AGCS.2024.20230078
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    Study on processing method of distributed scatterers radar interferometric phase information
    LI Shijin
    2024, 53(3):  588-588.  doi:10.11947/j.AGCS.2024.20230081
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