Mixed sparse representation for approximated observation-based compressed sensing radar imaging

doi:10.1117/1.JRS.12.035015

HFCAS OpenIR

	Mixed sparse representation for approximated observation-based compressed sensing radar imaging
	Li, Bo1,2 ; Liu, Falin 1,2; Zhou, Chongbin 1,2; Wang, Zheng 1,2; Han, Hao 1,2
	2018-09-10
发表期刊	JOURNAL OF APPLIED REMOTE SENSING
ISSN	1931-3195
通讯作者	Liu, Falin(liufl@ustc.edu.cn)
摘要	Recently, compressed sensing (CS) has been applied in synthetic aperture radar (SAR). A framework of mixed sparse representation (MSR) has been proposed for reconstructing SAR images due to the complicated ground features. The existing method decomposes the image into the point and smooth components, where the sparse constraint is directly applied to the smooth components. This makes it difficult to tackle the complex-valued SAR images, since the phase angles of SAR images are always stochastic. A magnitude-phase separation MSR method is proposed for CS-SAR imaging based on approximated observation. Compared to the existing method, the proposed method has better reconstruction ability, because it only imposes the sparse constraint on the magnitude of the smooth components, and therefore, the phase angles are still stochastic. Furthermore, owing to the inherent low memory requirement of approximated observation, the proposed method requires much less memory cost. In the simulation and experimental results, the proposed method deals with the complex-valued SAR images effectively and demonstrates superior performance over the chirp scaling algorithm and the existing MSR method. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
关键词	synthetic aperture radar mixed sparse representation compressed sensing magnitude-phase separation approximated observation
DOI	10.1117/1.JRS.12.035015
关键词[WOS]	GROUND-PENETRATING RADAR ; SAR ; ALGORITHM ; REGULARIZATION ; GENERATION ; ERROR
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61431016] ; National Natural Science Foundation of China[61771446] ; National Natural Science Foundation of China[61431016] ; National Natural Science Foundation of China[61771446]
项目资助者	National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Natural Science Foundation of China
WOS研究方向	Environmental Sciences & Ecology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000444125200001
出版者	SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.hfcas.ac.cn:8080/handle/334002/38731
专题	中国科学院合肥物质科学研究院
通讯作者	Liu, Falin
作者单位	1.Univ Sci & Technol China, Dept Elect Engn & Informat Sci, Hefei, Anhui, Peoples R China 2.Chinese Acad Sci, Key Lab Electromagnet Space Informat, Hefei, Anhui, Peoples R China
推荐引用方式 GB/T 7714	Li, Bo,Liu, Falin,Zhou, Chongbin,et al. Mixed sparse representation for approximated observation-based compressed sensing radar imaging[J]. JOURNAL OF APPLIED REMOTE SENSING,2018,12(3):20.
APA	Li, Bo,Liu, Falin,Zhou, Chongbin,Wang, Zheng,&Han, Hao.(2018).Mixed sparse representation for approximated observation-based compressed sensing radar imaging.JOURNAL OF APPLIED REMOTE SENSING,12(3),20.
MLA	Li, Bo,et al."Mixed sparse representation for approximated observation-based compressed sensing radar imaging".JOURNAL OF APPLIED REMOTE SENSING 12.3(2018):20.