Graph-Regularized Low-Rank Representation for Destriping of Hyperspectral Images

doi:10.1109/TGRS.2012.2226730

OPT OpenIR > 光谱成像技术研究室

	Graph-Regularized Low-Rank Representation for Destriping of Hyperspectral Images
	Lu, Xiaoqiang1 ; Wang, Yulong 2; Yuan, Yuan1
作者部门	光学影像学习与分析中心
	2013-07-01
发表期刊	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN	0196-2892
卷号	51 期号:7 页码:4009-4018
产权排序	1
摘要	Hyperspectral image destriping is a challenging and promising theme in remote sensing. Striping noise is a ubiquitous phenomenon in hyperspectral imagery, which may severely degrade the visual quality. A variety of methods have been proposed to effectively alleviate the effects of the striping noise. However, most of them fail to take full advantage of the high spectral correlation between the observation subimages in distinct bands and consider the local manifold structure of the hyperspectral data space. In order to remedy this drawback, in this paper, a novel graph-regularized low-rank representation (LRR) destriping algorithm is proposed by incorporating the LRR technique. To obtain desired destriping performance, two sides of performing destriping are included: 1) To exploit the high spectral correlation between the observation subimages in distinct bands, the technique of LRR is first utilized for destriping, and 2) to preserve the intrinsic local structure of the original hyperspectral data, the graph regularizer is incorporated in the objective function. The experimental results and quantitative analysis demonstrate that the proposed method can both remove striping noise and achieve cleaner and higher contrast reconstructed results.
文章类型	Article
关键词	Destriping Graph Regularizer Hyperspectral Image Low-rank Representation (Lrr) Spectral Correlation
WOS标题词	Science & Technology ; Physical Sciences ; Technology
DOI	10.1109/TGRS.2012.2226730
收录类别	SCI ; EI
关键词[WOS]	LANDSAT MSS IMAGES ; HISTOGRAM-MODIFICATION ; STRIPING REMOVAL ; MODIS DATA ; NOISE ; ALGORITHM ; REDUCTION ; TRANSFORM ; PURSUIT
语种	英语
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
项目资助者	National Basic Research Program of China (973 Program)(2011CB707104) ; National Natural Science Foundation of China(61100079 ; Postdoctoral Science Foundation of China(Y11I971400) ; 61172143)
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000320942600018
引用统计	被引频次：276[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.opt.ac.cn/handle/181661/23181
专题	光谱成像技术研究室
作者单位	1.Chinese Acad Sci, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Ctr Opt Imagery Anal & Learning, Xian 710119, Peoples R China 2.Hubei Univ, Fac Math & Comp Sci, Wuhan 430062, Peoples R China
推荐引用方式 GB/T 7714	Lu, Xiaoqiang,Wang, Yulong,Yuan, Yuan. Graph-Regularized Low-Rank Representation for Destriping of Hyperspectral Images[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2013,51(7):4009-4018.
APA	Lu, Xiaoqiang,Wang, Yulong,&Yuan, Yuan.(2013).Graph-Regularized Low-Rank Representation for Destriping of Hyperspectral Images.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,51(7),4009-4018.
MLA	Lu, Xiaoqiang,et al."Graph-Regularized Low-Rank Representation for Destriping of Hyperspectral Images".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 51.7(2013):4009-4018.

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