3D Imaging for Array InSAR Based on Gaussian Mixture Model Clustering
Array InSAR can generate 3D point clouds with the use of SAR images of the observed scene, which are obtained using multiple channels in a single flight. Its resolution power in elevation enables one to solve the layover problem. However, due to the limited number of arrays and the short baseline le...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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China Science Publishing & Media Ltd. (CSPM)
2017-12-01
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| Series: | Leida xuebao |
| Subjects: | |
| Online Access: | http://radars.ie.ac.cn/fileup/HTML/R17020.htm |
| _version_ | 1797428823263805440 |
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| author | Li Hang Liang Xingdong Zhang Fubo Wu Yirong |
| author_facet | Li Hang Liang Xingdong Zhang Fubo Wu Yirong |
| author_sort | Li Hang |
| collection | DOAJ |
| description | Array InSAR can generate 3D point clouds with the use of SAR images of the observed scene, which are obtained using multiple channels in a single flight. Its resolution power in elevation enables one to solve the layover problem. However, due to the limited number of arrays and the short baseline length, the resolution power in elevation is restricted. Together with the layover phenomenon of the urban buildings, the result of 3D reconstruction suffers from poor accuracy in positioning, and it is difficult to extract the effective characteristics of the buildings. In view of this situation, this paper proposed a 3D reconstruction method of array InSAR based on Gaussian mixture model clustering. First, the 3D point clouds of the observed scene are obtained by an algorithm with super-resolution based on compressive sensing, and then the scatters of buildings are extracted by density estimation; after which the method of Gaussian mixture model clustering is used to classify the 3D point clouds of the buildings. Finally, the inverse SAR images of each region are obtained by using the system parameters, and the 3D reconstruction of the buildings is completed. Based on the actual data of the first domestic 3D imaging experiment by airborne array InSAR, the validity of the algorithm is confirmed and the 3D imaging results of the buildings are obtained. |
| first_indexed | 2024-03-09T09:04:23Z |
| format | Article |
| id | doaj.art-a81c642563e44b89af5a70abfe993684 |
| institution | Directory Open Access Journal |
| issn | 2095-283X 2095-283X |
| language | English |
| last_indexed | 2024-03-09T09:04:23Z |
| publishDate | 2017-12-01 |
| publisher | China Science Publishing & Media Ltd. (CSPM) |
| record_format | Article |
| series | Leida xuebao |
| spelling | doaj.art-a81c642563e44b89af5a70abfe9936842023-12-02T10:54:01ZengChina Science Publishing & Media Ltd. (CSPM)Leida xuebao2095-283X2095-283X2017-12-016663063910.12000/JR170203D Imaging for Array InSAR Based on Gaussian Mixture Model ClusteringLi Hang0Liang Xingdong1Zhang Fubo2Wu Yirong3①(Science and Technology on Microwave Imaging Laboratory, Beijing 100190, China) ②(Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China) ③(University of Chinese Academy of Sciences, Beijing 100049, China)①(Science and Technology on Microwave Imaging Laboratory, Beijing 100190, China) ②(Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)①(Science and Technology on Microwave Imaging Laboratory, Beijing 100190, China) ②(Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)①(Science and Technology on Microwave Imaging Laboratory, Beijing 100190, China) ②(Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China)Array InSAR can generate 3D point clouds with the use of SAR images of the observed scene, which are obtained using multiple channels in a single flight. Its resolution power in elevation enables one to solve the layover problem. However, due to the limited number of arrays and the short baseline length, the resolution power in elevation is restricted. Together with the layover phenomenon of the urban buildings, the result of 3D reconstruction suffers from poor accuracy in positioning, and it is difficult to extract the effective characteristics of the buildings. In view of this situation, this paper proposed a 3D reconstruction method of array InSAR based on Gaussian mixture model clustering. First, the 3D point clouds of the observed scene are obtained by an algorithm with super-resolution based on compressive sensing, and then the scatters of buildings are extracted by density estimation; after which the method of Gaussian mixture model clustering is used to classify the 3D point clouds of the buildings. Finally, the inverse SAR images of each region are obtained by using the system parameters, and the 3D reconstruction of the buildings is completed. Based on the actual data of the first domestic 3D imaging experiment by airborne array InSAR, the validity of the algorithm is confirmed and the 3D imaging results of the buildings are obtained.http://radars.ie.ac.cn/fileup/HTML/R17020.htm3D reconstructionArray InSARLayover phenomenonCompressive Sensing (CS)Gaussian Mixture Model clustering (GMM clustering) |
| spellingShingle | Li Hang Liang Xingdong Zhang Fubo Wu Yirong 3D Imaging for Array InSAR Based on Gaussian Mixture Model Clustering Leida xuebao 3D reconstruction Array InSAR Layover phenomenon Compressive Sensing (CS) Gaussian Mixture Model clustering (GMM clustering) |
| title | 3D Imaging for Array InSAR Based on Gaussian Mixture Model Clustering |
| title_full | 3D Imaging for Array InSAR Based on Gaussian Mixture Model Clustering |
| title_fullStr | 3D Imaging for Array InSAR Based on Gaussian Mixture Model Clustering |
| title_full_unstemmed | 3D Imaging for Array InSAR Based on Gaussian Mixture Model Clustering |
| title_short | 3D Imaging for Array InSAR Based on Gaussian Mixture Model Clustering |
| title_sort | 3d imaging for array insar based on gaussian mixture model clustering |
| topic | 3D reconstruction Array InSAR Layover phenomenon Compressive Sensing (CS) Gaussian Mixture Model clustering (GMM clustering) |
| url | http://radars.ie.ac.cn/fileup/HTML/R17020.htm |
| work_keys_str_mv | AT lihang 3dimagingforarrayinsarbasedongaussianmixturemodelclustering AT liangxingdong 3dimagingforarrayinsarbasedongaussianmixturemodelclustering AT zhangfubo 3dimagingforarrayinsarbasedongaussianmixturemodelclustering AT wuyirong 3dimagingforarrayinsarbasedongaussianmixturemodelclustering |