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Graph-Regularized Low-Rank Representation for Destriping of Hyperspectral Images
Lu, Xiaoqiang1; Wang, Yulong2; Yuan, Yuan1
Department光学影像学习与分析中心
2013-07-01
Source PublicationIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
ISSN0196-2892
Volume51Issue:7Pages:4009-4018
Contribution Rank1
AbstractHyperspectral 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.
SubtypeArticle
KeywordDestriping Graph Regularizer Hyperspectral Image Low-rank Representation (Lrr) Spectral Correlation
WOS HeadingsScience & Technology ; Physical Sciences ; Technology
DOI10.1109/TGRS.2012.2226730
Indexed BySCI ; EI
WOS KeywordLANDSAT MSS IMAGES ; HISTOGRAM-MODIFICATION ; STRIPING REMOVAL ; MODIS DATA ; NOISE ; ALGORITHM ; REDUCTION ; TRANSFORM ; PURSUIT
Language英语
WOS Research AreaGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
Funding OrganizationNational Basic Research Program of China (973 Program)(2011CB707104) ; National Natural Science Foundation of China(61100079 ; Postdoctoral Science Foundation of China(Y11I971400) ; 61172143)
WOS SubjectGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000320942600018
Citation statistics
Cited Times:139[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/23181
Collection光学影像学习与分析中心
Affiliation1.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
Recommended Citation
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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