Academic Journal
Deep SAR Tomography: A Model-Inspired Approach With Learned Sparse Regularizer
| Title: | Deep SAR Tomography: A Model-Inspired Approach With Learned Sparse Regularizer |
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| Authors: | Rong Shen, Mou Wang, Jiangbo Hu, Yanbo Wen, Shunjun Wei, Xiaoling Zhang, Ling Fan |
| Source: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 18870-18881 (2024) |
| Publisher Information: | IEEE, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Ocean engineering LCC:Geophysics. Cosmic physics |
| Subject Terms: | Compressed sensing (CS), synthetic aperture radar tomography (TomoSAR), shrinkage thresholding, unfolded deep neural work, Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809 |
| More Details: | Synthetic aperture radar tomography (TomoSAR) can acquire high resolution in height direction by forming a large synthetic aperture along the tomographic direction. Compressed sensing (CS) is widely utilized in TomoSAR imaging to reduce the costs of data sensing. Nevertheless, traditional CS-based algorithms are limited to computational complexity and the nontrivial parameters' tuning. To address such problems, an efficient unfolded deep shrinkage-thresholding network is proposed for TomoSAR imaging in this article. The proposed method adopts convolutional neural network module to learn a generalized nonlinear sparse transformation operator, showing great benefits in exploring the optimal prior. Besides, the hyperparameters of the optimization framework are learned by end-to-end learning mechanism instead of manual-defined, which obviously improves the efficiency of imaging process. Inspired by residual network, the residual learning is introduced to reconstruction blocks of the proposed imaging network, improving the robustness of the network. In addition, the training dataset is constructed from point cloud data based on TomoSAR imaging principles, enhancing the network's ability to extract structural information. Finally, extensive simulation and measured experimental results show the effectiveness of the proposed method, obtaining high-quality imaging results while maintaining high computational efficiency. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1939-1404 2151-1535 |
| Relation: | https://ieeexplore.ieee.org/document/10713215/; https://doaj.org/toc/1939-1404; https://doaj.org/toc/2151-1535 |
| DOI: | 10.1109/JSTARS.2024.3477989 |
| Access URL: | https://doaj.org/article/3caf7afe410f4ea09eb3c18ba1a750e1 |
| Accession Number: | edsdoj.3caf7afe410f4ea09eb3c18ba1a750e1 |
| Database: | Directory of Open Access Journals |
| ISSN: | 19391404 21511535 |
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| DOI: | 10.1109/JSTARS.2024.3477989 |
| Published in: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Language: | English |