Unsupervised TomoSAR Image Reconstruction Through Virtual Multiple Measurement Explorations
Tomographic synthetic aperture radar (TomoSAR) reconstruction produces 3D imaging of scenes from measurements and has recently been combined with data-driven deep learning techniques. While most existing methods rely on supervised learning with simulation-based and paired training samples, we focus...
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| Veröffentlicht in: | Proceedings of the IEEE National Radar Conference (1996) S. 321 - 326 |
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04.10.2025
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| ISSN: | 2375-5318 |
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| Abstract | Tomographic synthetic aperture radar (TomoSAR) reconstruction produces 3D imaging of scenes from measurements and has recently been combined with data-driven deep learning techniques. While most existing methods rely on supervised learning with simulation-based and paired training samples, we focus on unsupervised learning that operates without such samples. This approach promises more robust, flexible, and generalized reconstruction capabilities while leveraging deep learning to enhance reconstruction quality. We present a preliminary exploration using virtual multiple measurements derived from raw measurements. We leverage the recent finding that imaging areas possess both non-local and local similarities, resulting in highly correlated measurements. This property enables us to obtain additional virtual measurements beyond the raw data. By treating these measurements as multiple independent noisy samples of the same underlying signal, we propose a self-supervised robust reconstruction loss that incorporates an integrated physics forward measurement model. Our approach feeds measurement pairs to both the input and output of the reconstruction neural network, enabling it to explore the underlying signal while suppressing noise. We also adopt an encoderdecoder network architecture that implicitly incorporates scene priors through its inductive bias. By combining the physics model prior and scene prior while remaining robust to noise, we achieve high-fidelity reconstruction results. Experiments on both simulated and measured data demonstrate our method's effectiveness, achieving reconstruction performance comparable to supervised approaches while showing better generalization under varying noise conditions. |
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| AbstractList | Tomographic synthetic aperture radar (TomoSAR) reconstruction produces 3D imaging of scenes from measurements and has recently been combined with data-driven deep learning techniques. While most existing methods rely on supervised learning with simulation-based and paired training samples, we focus on unsupervised learning that operates without such samples. This approach promises more robust, flexible, and generalized reconstruction capabilities while leveraging deep learning to enhance reconstruction quality. We present a preliminary exploration using virtual multiple measurements derived from raw measurements. We leverage the recent finding that imaging areas possess both non-local and local similarities, resulting in highly correlated measurements. This property enables us to obtain additional virtual measurements beyond the raw data. By treating these measurements as multiple independent noisy samples of the same underlying signal, we propose a self-supervised robust reconstruction loss that incorporates an integrated physics forward measurement model. Our approach feeds measurement pairs to both the input and output of the reconstruction neural network, enabling it to explore the underlying signal while suppressing noise. We also adopt an encoderdecoder network architecture that implicitly incorporates scene priors through its inductive bias. By combining the physics model prior and scene prior while remaining robust to noise, we achieve high-fidelity reconstruction results. Experiments on both simulated and measured data demonstrate our method's effectiveness, achieving reconstruction performance comparable to supervised approaches while showing better generalization under varying noise conditions. |
| Author | Zeng, Tianjiao Wei, Shunjun Zhang, Xiaoling Zhan, Xu Liu, Liang Shi, Jun Wang, Mou |
| Author_xml | – sequence: 1 givenname: Liang surname: Liu fullname: Liu, Liang organization: University of Electronic Science and Technology of China,Chengdu,China,611731 – sequence: 2 givenname: Tianjiao surname: Zeng fullname: Zeng, Tianjiao email: tzeng@uestc.edu.cn organization: University of Electronic Science and Technology of China,Chengdu,China,611731 – sequence: 3 givenname: Mou surname: Wang fullname: Wang, Mou organization: University of Electronic Science and Technology of China,Chengdu,China,611731 – sequence: 4 givenname: Jun surname: Shi fullname: Shi, Jun organization: University of Electronic Science and Technology of China,Chengdu,China,611731 – sequence: 5 givenname: Shunjun surname: Wei fullname: Wei, Shunjun organization: University of Electronic Science and Technology of China,Chengdu,China,611731 – sequence: 6 givenname: Xiaoling surname: Zhang fullname: Zhang, Xiaoling organization: University of Electronic Science and Technology of China,Chengdu,China,611731 – sequence: 7 givenname: Xu surname: Zhan fullname: Zhan, Xu email: zhanxu@std.uestc.edu.cn organization: University of Electronic Science and Technology of China,Chengdu,China,611731 |
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| Snippet | Tomographic synthetic aperture radar (TomoSAR) reconstruction produces 3D imaging of scenes from measurements and has recently been combined with data-driven... |
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| SubjectTerms | data-driven Deep learning Image reconstruction Loss measurement neural network inductive bias Noise Noise measurement Physics Redundancy Three-dimensional displays tomoSAR Training Unsupervised learning |
| Title | Unsupervised TomoSAR Image Reconstruction Through Virtual Multiple Measurement Explorations |
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