A Novel Deep Unfolding Network for Multi-Band SAR Sparse Imaging and Autofocusing
The sparse imaging network of synthetic aperture radar (SAR) is usually designed end to end and has a limited adaptability to radar systems of different bands. Meanwhile, the implementation of the sparse imaging algorithm depends on the sparsity of the target scene and usually adopts a fixed <inl...
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MDPI AG
2025-04-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/7/1279 |
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| author | Xiaopeng Li Mengyang Zhan Yiheng Liang Yinwei Li Gang Xu Bingnan Wang |
| author_facet | Xiaopeng Li Mengyang Zhan Yiheng Liang Yinwei Li Gang Xu Bingnan Wang |
| author_sort | Xiaopeng Li |
| collection | DOAJ |
| description | The sparse imaging network of synthetic aperture radar (SAR) is usually designed end to end and has a limited adaptability to radar systems of different bands. Meanwhile, the implementation of the sparse imaging algorithm depends on the sparsity of the target scene and usually adopts a fixed <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>L</mi></mrow><mrow><mn>1</mn></mrow></msub></mrow></semantics></math></inline-formula> regularization solution, which has a mediocre reconstruction effect on complex scenes. In this paper, a novel SAR imaging deep unfolding network based on approximate observation is proposed for multi-band SAR systems. Firstly, the approximate observation module is separated from the optimal solution network model and selected according to the multi-band radar echo. Secondly, to realize the SAR imaging of non-sparse scenes, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>L</mi></mrow><mrow><mi>p</mi></mrow></msub></mrow></semantics></math></inline-formula> regularization is used to constrain the uncertain transform domain of the target scene. The adaptive optimization of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>L</mi></mrow><mrow><mi>p</mi></mrow></msub></mrow></semantics></math></inline-formula> parameters is realized by using a data-driven approach. Furthermore, considering that phase errors may be introduced in the real SAR system during echo acquisition, an error estimation module is added to the network to estimate and compensate for the phase errors. Finally, the results from both simulated and real data experiments demonstrate that the proposed method exhibits outstanding performance under 0.22 THz and 9.6 GHz echo data: high-resolution SAR focused images are achieved under four different sparsity conditions of 20%, 40%, 60%, and 80%. These results fully validate the strong adaptability and robustness of the proposed method to diverse SAR system configurations and complex target scenarios. |
| format | Article |
| id | doaj-art-5480fc92479d4239bcd3a117a68e3326 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-5480fc92479d4239bcd3a117a68e33262025-08-20T03:03:21ZengMDPI AGRemote Sensing2072-42922025-04-01177127910.3390/rs17071279A Novel Deep Unfolding Network for Multi-Band SAR Sparse Imaging and AutofocusingXiaopeng Li0Mengyang Zhan1Yiheng Liang2Yinwei Li3Gang Xu4Bingnan Wang5School of Optoelectronic Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Optoelectronic Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Optoelectronic Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaSchool of Optoelectronic Information and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, ChinaState Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, ChinaNational Key Laboratory of Microwave Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaThe sparse imaging network of synthetic aperture radar (SAR) is usually designed end to end and has a limited adaptability to radar systems of different bands. Meanwhile, the implementation of the sparse imaging algorithm depends on the sparsity of the target scene and usually adopts a fixed <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>L</mi></mrow><mrow><mn>1</mn></mrow></msub></mrow></semantics></math></inline-formula> regularization solution, which has a mediocre reconstruction effect on complex scenes. In this paper, a novel SAR imaging deep unfolding network based on approximate observation is proposed for multi-band SAR systems. Firstly, the approximate observation module is separated from the optimal solution network model and selected according to the multi-band radar echo. Secondly, to realize the SAR imaging of non-sparse scenes, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>L</mi></mrow><mrow><mi>p</mi></mrow></msub></mrow></semantics></math></inline-formula> regularization is used to constrain the uncertain transform domain of the target scene. The adaptive optimization of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>L</mi></mrow><mrow><mi>p</mi></mrow></msub></mrow></semantics></math></inline-formula> parameters is realized by using a data-driven approach. Furthermore, considering that phase errors may be introduced in the real SAR system during echo acquisition, an error estimation module is added to the network to estimate and compensate for the phase errors. Finally, the results from both simulated and real data experiments demonstrate that the proposed method exhibits outstanding performance under 0.22 THz and 9.6 GHz echo data: high-resolution SAR focused images are achieved under four different sparsity conditions of 20%, 40%, 60%, and 80%. These results fully validate the strong adaptability and robustness of the proposed method to diverse SAR system configurations and complex target scenarios.https://www.mdpi.com/2072-4292/17/7/1279synthetic aperture radar (SAR)sparse samplingapproximate observation operators<i>L<sub>p</sub></i> regularizationautofocusing |
| spellingShingle | Xiaopeng Li Mengyang Zhan Yiheng Liang Yinwei Li Gang Xu Bingnan Wang A Novel Deep Unfolding Network for Multi-Band SAR Sparse Imaging and Autofocusing Remote Sensing synthetic aperture radar (SAR) sparse sampling approximate observation operators <i>L<sub>p</sub></i> regularization autofocusing |
| title | A Novel Deep Unfolding Network for Multi-Band SAR Sparse Imaging and Autofocusing |
| title_full | A Novel Deep Unfolding Network for Multi-Band SAR Sparse Imaging and Autofocusing |
| title_fullStr | A Novel Deep Unfolding Network for Multi-Band SAR Sparse Imaging and Autofocusing |
| title_full_unstemmed | A Novel Deep Unfolding Network for Multi-Band SAR Sparse Imaging and Autofocusing |
| title_short | A Novel Deep Unfolding Network for Multi-Band SAR Sparse Imaging and Autofocusing |
| title_sort | novel deep unfolding network for multi band sar sparse imaging and autofocusing |
| topic | synthetic aperture radar (SAR) sparse sampling approximate observation operators <i>L<sub>p</sub></i> regularization autofocusing |
| url | https://www.mdpi.com/2072-4292/17/7/1279 |
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