Sparse Synthetic Aperture Radar Imaging From Compressed Sensing and Machine Learning: Theories, applications, and trends

被引:137
作者
Xu, Gang [1 ]
Zhang, Bangjie [1 ]
Yu, Hanwen [2 ]
Chen, Jianlai [3 ]
Xing, Mengdao [4 ]
Hong, Wei [1 ]
机构
[1] Southeast Univ, Sch Informat Sci & Engn, State Key Lab Millimeter Waves, Nanjing 210096, Peoples R China
[2] Univ Elect Sci & Technol China, Sch Resources & Environm, Chengdu 611731, Sichuan, Peoples R China
[3] Cent South Univ, Changsha 410083, Peoples R China
[4] Xidian Univ, Natl Lab Radar Signal Proc, Xian 410083, Peoples R China
基金
中国国家自然科学基金;
关键词
Radar polarimetry; Synthetic aperture radar; Imaging; Radar imaging; Image reconstruction; Focusing; Apertures; CONVOLUTIONAL NEURAL-NETWORK; LOW-RANK; MANEUVERING TARGETS; SAR TOMOGRAPHY; MIMO RADAR; INTERFEROMETRIC PHASE; SIGNAL RECOVERY; ARRAY SAR; PARAMETER-ESTIMATION; JOINT SPARSITY;
D O I
10.1109/MGRS.2022.3218801
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Synthetic aperture radar (SAR) image formation can be treated as a class of ill-posed linear inverse problems, and the resolution is limited by the data bandwidth for traditional imaging techniques via matched filter (MF). The sparse SAR imaging technology using compressed sensing (CS) has been developed for enhanced performance, such as superresolution, feature enhancement, etc. More recently, sparse SAR imaging from machine learning (ML), including deep learning (DL), has been further studied, showing great potential in the imaging area. However, there are still gaps between the two groups of methods for sparse SAR imaging, and their connections have not been established.
引用
收藏
页码:32 / 69
页数:38
相关论文
共 210 条
[1]   Wavelet-Based Compressed Sensing for SAR Tomography of Forested Areas [J].
Aguilera, Esteban ;
Nannini, Matteo ;
Reigber, Andreas .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2013, 51 (12) :5283-5295
[2]  
AMBROSANIO M, PROC 2020 14 EUR C A, P1, DOI DOI 10.23919/EUCAP48036.2020.9136081
[3]  
Amin M, 2015, COMPRESSIVE SENSING FOR URBAN RADAR, P1
[4]   Simultaneous Moving and Stationary Target Imaging for Geosynchronous Spaceborne-Airborne Bistatic SAR Based on Sparse Separation [J].
An, Hongyang ;
Wu, Junjie ;
Teh, Kah Chan ;
Sun, Zhichao ;
Yang, Jianyu .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2021, 59 (08) :6722-6735
[5]   Immersive Interactive SAR Image Representation Using Non-negative Matrix Factorization [J].
Babaee, Mohammadreza ;
Yu, Xuejie ;
Rigoll, Gerhard ;
Datcu, Mihai .
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2016, 9 (07) :2844-2853
[6]   Sequence SAR Image Classification Based on Bidirectional Convolution-Recurrent Network [J].
Bai, Xueru ;
Xue, Ruihang ;
Wang, Li ;
Zhou, Feng .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2019, 57 (11) :9223-9235
[7]   Robust Nonlocal Low-Rank SAR Time Series Despeckling Considering Speckle Correlation by Total Variation Regularization [J].
Baier, Gerald ;
He, Wei ;
Yokoya, Naoto .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2020, 58 (11) :7942-7954
[8]   DLSLA 3-D SAR Imaging Based on Reweighted Gridless Sparse Recovery Method [J].
Bao, Qian ;
Peng, Xueming ;
Wang, Zhirui ;
Lin, Yun ;
Hong, Wen .
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2016, 13 (06) :841-845
[9]  
Baraniuk Richard, 2007, 2007 IEEE Radar Conference, P128, DOI 10.1109/RADAR.2007.374203
[10]   Model-Based Compressive Sensing [J].
Baraniuk, Richard G. ;
Cevher, Volkan ;
Duarte, Marco F. ;
Hegde, Chinmay .
IEEE TRANSACTIONS ON INFORMATION THEORY, 2010, 56 (04) :1982-2001