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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