Tomographic SAR Imaging Method Based on Sparse and Low-rank Structures
This paper proposes a three-dimensional tomographic SAR imaging method based on a combined sparse and low-rank structures. The traditional Compressed Sensing (CS) based tomographic SAR imaging methods only utilize the sparse representation and reconstruct along the elevation axis of a given azimuth-...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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China Science Publishing & Media Ltd. (CSPM)
2022-02-01
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| Series: | Leida xuebao |
| Subjects: | |
| Online Access: | https://radars.ac.cn/cn/article/doi/10.12000/JR21210 |
| _version_ | 1797426322498125824 |
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| author | Yao ZHAO Juncong XU Xiangyin QUAN Li CUI Zhe ZHANG |
| author_facet | Yao ZHAO Juncong XU Xiangyin QUAN Li CUI Zhe ZHANG |
| author_sort | Yao ZHAO |
| collection | DOAJ |
| description | This paper proposes a three-dimensional tomographic SAR imaging method based on a combined sparse and low-rank structures. The traditional Compressed Sensing (CS) based tomographic SAR imaging methods only utilize the sparse representation and reconstruct along the elevation axis of a given azimuth-distance unit. Considering that the target distributions in cities, forests, and other cases are relatively similar, the elevation backscattering patterns of adjacent azimuth-range cells (pixels) are expected to be highly correlated. The proposed method introduces the Karhunen-Loeve transform to characterize the low-rank structures of the elevation of the target areas and constructs a tomographic SAR imaging model that combines sparse and low-rank structures. The ADMM algorithm is applied to solve the tomographic SAR imaging model, the complex original optimization problem is decomposed into several relatively simple sub-problems, and the tomographic SAR imaging results are obtained by the alternate projection of optimization variables. This method improves the reconstruction accuracy in the case of a few interferograms or channels and has better imaging performance. Simulations and real data experiments show that the reconstruction method can effectively separate the scatterers and ensure the accuracy of the reconstruction energy, maintain a good imaging performance under the condition of reducing the number of interferograms or channels, and effectively suppress the artifacts. |
| first_indexed | 2024-03-09T08:28:34Z |
| format | Article |
| id | doaj.art-363a5a3445054f8caf4aff654a5ca12d |
| institution | Directory Open Access Journal |
| issn | 2095-283X |
| language | English |
| last_indexed | 2024-03-09T08:28:34Z |
| publishDate | 2022-02-01 |
| publisher | China Science Publishing & Media Ltd. (CSPM) |
| record_format | Article |
| series | Leida xuebao |
| spelling | doaj.art-363a5a3445054f8caf4aff654a5ca12d2023-12-02T20:25:26ZengChina Science Publishing & Media Ltd. (CSPM)Leida xuebao2095-283X2022-02-01111526110.12000/JR21210R21210Tomographic SAR Imaging Method Based on Sparse and Low-rank StructuresYao ZHAO0Juncong XU1Xiangyin QUAN2Li CUI3Zhe ZHANG4Guangdong University of Technology, Guangzhou 510006, ChinaGuangdong University of Technology, Guangzhou 510006, ChinaChina Academy of Launch Vehicle Technology, Beijing 100076, ChinaBeijing Institute of Remote Sensing, Beijing 100192, ChinaSuzhou Aerospace Information Research Institute, Suzhou 215000, ChinaThis paper proposes a three-dimensional tomographic SAR imaging method based on a combined sparse and low-rank structures. The traditional Compressed Sensing (CS) based tomographic SAR imaging methods only utilize the sparse representation and reconstruct along the elevation axis of a given azimuth-distance unit. Considering that the target distributions in cities, forests, and other cases are relatively similar, the elevation backscattering patterns of adjacent azimuth-range cells (pixels) are expected to be highly correlated. The proposed method introduces the Karhunen-Loeve transform to characterize the low-rank structures of the elevation of the target areas and constructs a tomographic SAR imaging model that combines sparse and low-rank structures. The ADMM algorithm is applied to solve the tomographic SAR imaging model, the complex original optimization problem is decomposed into several relatively simple sub-problems, and the tomographic SAR imaging results are obtained by the alternate projection of optimization variables. This method improves the reconstruction accuracy in the case of a few interferograms or channels and has better imaging performance. Simulations and real data experiments show that the reconstruction method can effectively separate the scatterers and ensure the accuracy of the reconstruction energy, maintain a good imaging performance under the condition of reducing the number of interferograms or channels, and effectively suppress the artifacts.https://radars.ac.cn/cn/article/doi/10.12000/JR21210three-dimensional (3-d) imagingsar tomographysparselow-rankkarhunen loeve transform |
| spellingShingle | Yao ZHAO Juncong XU Xiangyin QUAN Li CUI Zhe ZHANG Tomographic SAR Imaging Method Based on Sparse and Low-rank Structures Leida xuebao three-dimensional (3-d) imaging sar tomography sparse low-rank karhunen loeve transform |
| title | Tomographic SAR Imaging Method Based on Sparse and Low-rank Structures |
| title_full | Tomographic SAR Imaging Method Based on Sparse and Low-rank Structures |
| title_fullStr | Tomographic SAR Imaging Method Based on Sparse and Low-rank Structures |
| title_full_unstemmed | Tomographic SAR Imaging Method Based on Sparse and Low-rank Structures |
| title_short | Tomographic SAR Imaging Method Based on Sparse and Low-rank Structures |
| title_sort | tomographic sar imaging method based on sparse and low rank structures |
| topic | three-dimensional (3-d) imaging sar tomography sparse low-rank karhunen loeve transform |
| url | https://radars.ac.cn/cn/article/doi/10.12000/JR21210 |
| work_keys_str_mv | AT yaozhao tomographicsarimagingmethodbasedonsparseandlowrankstructures AT juncongxu tomographicsarimagingmethodbasedonsparseandlowrankstructures AT xiangyinquan tomographicsarimagingmethodbasedonsparseandlowrankstructures AT licui tomographicsarimagingmethodbasedonsparseandlowrankstructures AT zhezhang tomographicsarimagingmethodbasedonsparseandlowrankstructures |