3-D SAR Data-Driven Imaging via Learned Low-Rank and Sparse Priors

In the research topic of three-dimensional (3-D) synthetic aperture radar (SAR) imaging, the sparsity-enforcing techniques offer promise in shortening the sensing time and improving the reconstruction accuracy. However, many of them only explore the sparse prior of 3-D SAR images, which leads to bia...

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Vydané v:	IEEE transactions on geoscience and remote sensing Ročník 60; s. 1 - 17
Hlavní autori:	Wang, Mou, Wei, Shunjun, Zhou, Zichen, Shi, Jun, Zhang, Xiaoling, Guo, Yongxin
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	3-D synthetic aperture radar (SAR) imaging Algorithms Computational modeling deep unfolding Echoes fast iterative shrinkage/thresholding algorithm (FISTA) Image reconstruction Imaging Imaging techniques Independent variables low-rank matrix completion millimeter-wave (mmW) Optimization Radar imaging Radar polarimetry SAR (radar) Scattering Synthetic aperture radar Three-dimensional displays
ISSN:	0196-2892, 1558-0644
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Abstract	In the research topic of three-dimensional (3-D) synthetic aperture radar (SAR) imaging, the sparsity-enforcing techniques offer promise in shortening the sensing time and improving the reconstruction accuracy. However, many of them only explore the sparse prior of 3-D SAR images, which leads to biased estimations in cases of non-sparse scenarios. To remedy this problem, we propose a new network with learned low-rank and sparse priors, i.e., LLRS-Net, to obtain improved reconstructions from sparsely sampled 3-D SAR echoes. In our scheme, a two-stage reconstruction algorithmic framework (LSRA) is derived based on sparse and low-rank priors, wherein the first stage recovers the measurements from their limited observations by exploring the low-rank prior, while the second estimates the final 3-D SAR images with a fast iterative optimization. Theoretically inspired by LRSA, the LLRS-Net is designed into a cascaded network structure. In LLRS-Net, the trainable weights serve as independent variables and control the algorithmic hyperparameters via regularizing functions, ensuring a well-conditioned updating tendency. By end-to-end training, the network weights are updated automatically under the guidance of a compound loss function constraining both the outputs of two stages. Finally, the methodology is validated on simulations and measured experiments. These results show that the proposed framework outperforms many state-of-the-art imaging algorithms in recovering 3-D SAR images from incomplete echo data.
AbstractList	In the research topic of three-dimensional (3-D) synthetic aperture radar (SAR) imaging, the sparsity-enforcing techniques offer promise in shortening the sensing time and improving the reconstruction accuracy. However, many of them only explore the sparse prior of 3-D SAR images, which leads to biased estimations in cases of non-sparse scenarios. To remedy this problem, we propose a new network with learned low-rank and sparse priors, i.e., LLRS-Net, to obtain improved reconstructions from sparsely sampled 3-D SAR echoes. In our scheme, a two-stage reconstruction algorithmic framework (LSRA) is derived based on sparse and low-rank priors, wherein the first stage recovers the measurements from their limited observations by exploring the low-rank prior, while the second estimates the final 3-D SAR images with a fast iterative optimization. Theoretically inspired by LRSA, the LLRS-Net is designed into a cascaded network structure. In LLRS-Net, the trainable weights serve as independent variables and control the algorithmic hyperparameters via regularizing functions, ensuring a well-conditioned updating tendency. By end-to-end training, the network weights are updated automatically under the guidance of a compound loss function constraining both the outputs of two stages. Finally, the methodology is validated on simulations and measured experiments. These results show that the proposed framework outperforms many state-of-the-art imaging algorithms in recovering 3-D SAR images from incomplete echo data.
Author	Shi, Jun Zhou, Zichen Wang, Mou Wei, Shunjun Zhang, Xiaoling Guo, Yongxin
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Cites_doi	10.1109/LGRS.2014.2372319 10.1007/s11263-016-0930-5 10.1109/MSP.2014.2312834 10.1109/TGRS.2021.3110579 10.1109/TMI.2021.3054167 10.1109/TPAMI.2012.271 10.1109/TIP.2018.2821925 10.1109/MSP.2014.2312098 10.1109/MSP.2020.3016905 10.1109/TIP.2019.2927458 10.1109/TGRS.2012.2191293 10.1109/TSP.2017.2711501 10.1109/JSTARS.2013.2238891 10.1109/JSEN.2020.3025053 10.1109/CVPR.2018.00196 10.1109/TSP.2021.3076900 10.1109/TGRS.2021.3093307 10.1109/TGRS.1983.350489 10.1137/080738970 10.1016/j.isprsjprs.2021.03.004 10.1109/MGRS.2013.2248301 10.1109/LGRS.2015.2499445 10.1109/JSTARS.2020.3017487 10.1109/TGRS.2021.3073123 10.1109/TGRS.2021.3068405 10.1109/JPROC.2009.2037526 10.1109/MAES.2013.6575407 10.1016/j.neucom.2013.03.017 10.1109/TSP.2020.3032231 10.1016/j.isprsjprs.2015.10.003 10.1109/TGRS.2022.3147472 10.1117/12.876541 10.1109/TIP.2021.3104168 10.1109/TGRS.2020.3011631 10.1109/TMI.2021.3096218 10.1109/TNNLS.2020.2978017 10.2528/PIER11033105 10.1109/TMTT.2017.2772862 10.1109/JMW.2020.3035790 10.1049/el.2016.1168 10.1109/TGRS.2008.2001170 10.1109/5.726791 10.1109/8.855491 10.1109/JSTARS.2020.3014696 10.1109/TGRS.2021.3139914 10.1049/iet-rsn.2009.0235 10.3390/rs12203283 10.1002/9781119538875 10.1109/TGRS.2017.2771826 10.1109/TGRS.2014.2364525 10.1109/JSTARS.2021.3139594 10.1109/JSTARS.2013.2263309
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References	ref13 ref12 ref15 ref14 ref53 ref52 ref11 ref55 ref10 ref54 ref17 ref16 ref19 ref18 Curlander (ref1) 1991; 11 Qian (ref42) 2021 ref51 ref50 ref46 ref45 ref48 ref47 Jain (ref56); 23 ref41 ref44 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 ref35 ref34 ref37 ref36 ref30 ref33 ref32 ref2 ref39 ref38 ref24 ref23 ref26 ref25 ref20 ref22 ref21 ref28 ref27 ref29 Karakuş (ref31) 2019
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SubjectTerms	3-D synthetic aperture radar (SAR) imaging Algorithms Computational modeling deep unfolding Echoes fast iterative shrinkage/thresholding algorithm (FISTA) Image reconstruction Imaging Imaging techniques Independent variables low-rank matrix completion millimeter-wave (mmW) Optimization Radar imaging Radar polarimetry SAR (radar) Scattering Synthetic aperture radar Three-dimensional displays
Title	3-D SAR Data-Driven Imaging via Learned Low-Rank and Sparse Priors
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