Array Three-Dimensional SAR Imaging via Composite Low-Rank and Sparse Prior
Array three-dimensional (3D) synthetic aperture radar (SAR) imaging has been used for 3D modeling of urban buildings and diagnosis of target scattering characteristics, and represents one of the significant directions in SAR development in recent years. However, sparse driven 3D imaging methods usua...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/2/321 |
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| author | Zhiliang Yang Yangyang Wang Chudi Zhang Xu Zhan Guohao Sun Yuxuan Liu Yuru Mao |
| author_facet | Zhiliang Yang Yangyang Wang Chudi Zhang Xu Zhan Guohao Sun Yuxuan Liu Yuru Mao |
| author_sort | Zhiliang Yang |
| collection | DOAJ |
| description | Array three-dimensional (3D) synthetic aperture radar (SAR) imaging has been used for 3D modeling of urban buildings and diagnosis of target scattering characteristics, and represents one of the significant directions in SAR development in recent years. However, sparse driven 3D imaging methods usually only capture the sparse features of the imaging scene, which can result in the loss of the structural information of the target and cause bias effects, affecting the imaging quality. To address this issue, we propose a novel array 3D SAR imaging method based on composite sparse and low-rank prior (SLRP), which can achieve high-quality imaging even with limited observation data. Firstly, an imaging optimization model based on composite SLRP is established, which captures both sparse and low-rank features simultaneously by combining non-convex regularization functions and improved nuclear norm (INN), reducing bias effects during the imaging process and improving imaging accuracy. Then, the framework that integrates variable splitting and alternative minimization (VSAM) is presented to solve the imaging optimization problem, which is suitable for high-dimensional imaging scenes. Finally, the performance of the method is validated through extensive simulation and real data experiments. The results indicate that the proposed method can significantly improve imaging quality with limited observational data. |
| format | Article |
| id | doaj-art-5fa43dafb2044a3cb3e6bc4af4e9b9b1 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-5fa43dafb2044a3cb3e6bc4af4e9b9b12025-01-24T13:48:07ZengMDPI AGRemote Sensing2072-42922025-01-0117232110.3390/rs17020321Array Three-Dimensional SAR Imaging via Composite Low-Rank and Sparse PriorZhiliang Yang0Yangyang Wang1Chudi Zhang2Xu Zhan3Guohao Sun4Yuxuan Liu5Yuru Mao6Shanxi Province Key Laboratory of Intelligent Detection Technology & Equipment, North University of China, Taiyuan 030051, ChinaShanxi Province Key Laboratory of Intelligent Detection Technology & Equipment, North University of China, Taiyuan 030051, ChinaIntelligent Game and Decision Laboratory, Beijing 100091, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Aeronautics and Astronautics, Sichuan University, Chengdu 611731, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaShanxi Province Key Laboratory of Intelligent Detection Technology & Equipment, North University of China, Taiyuan 030051, ChinaArray three-dimensional (3D) synthetic aperture radar (SAR) imaging has been used for 3D modeling of urban buildings and diagnosis of target scattering characteristics, and represents one of the significant directions in SAR development in recent years. However, sparse driven 3D imaging methods usually only capture the sparse features of the imaging scene, which can result in the loss of the structural information of the target and cause bias effects, affecting the imaging quality. To address this issue, we propose a novel array 3D SAR imaging method based on composite sparse and low-rank prior (SLRP), which can achieve high-quality imaging even with limited observation data. Firstly, an imaging optimization model based on composite SLRP is established, which captures both sparse and low-rank features simultaneously by combining non-convex regularization functions and improved nuclear norm (INN), reducing bias effects during the imaging process and improving imaging accuracy. Then, the framework that integrates variable splitting and alternative minimization (VSAM) is presented to solve the imaging optimization problem, which is suitable for high-dimensional imaging scenes. Finally, the performance of the method is validated through extensive simulation and real data experiments. The results indicate that the proposed method can significantly improve imaging quality with limited observational data.https://www.mdpi.com/2072-4292/17/2/321three-dimensional (3D) imagingsynthetic aperture radar (SAR)alternative minimizationlow-ranknon-convex |
| spellingShingle | Zhiliang Yang Yangyang Wang Chudi Zhang Xu Zhan Guohao Sun Yuxuan Liu Yuru Mao Array Three-Dimensional SAR Imaging via Composite Low-Rank and Sparse Prior Remote Sensing three-dimensional (3D) imaging synthetic aperture radar (SAR) alternative minimization low-rank non-convex |
| title | Array Three-Dimensional SAR Imaging via Composite Low-Rank and Sparse Prior |
| title_full | Array Three-Dimensional SAR Imaging via Composite Low-Rank and Sparse Prior |
| title_fullStr | Array Three-Dimensional SAR Imaging via Composite Low-Rank and Sparse Prior |
| title_full_unstemmed | Array Three-Dimensional SAR Imaging via Composite Low-Rank and Sparse Prior |
| title_short | Array Three-Dimensional SAR Imaging via Composite Low-Rank and Sparse Prior |
| title_sort | array three dimensional sar imaging via composite low rank and sparse prior |
| topic | three-dimensional (3D) imaging synthetic aperture radar (SAR) alternative minimization low-rank non-convex |
| url | https://www.mdpi.com/2072-4292/17/2/321 |
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