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Structured Sparse Method for Hyperspectral Unmixing
Zhu, Feiyun; Wang, Ying; Xiang, Shiming; Fan, Bin; Pan, Chunhong
Source PublicationISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
2014-02-01
Volume88Pages:101-118
SubtypeArticle
AbstractHyperspectral Unmixing (HU) has received increasing attention in the past decades due to its ability of unveiling information latent in hyperspectral data. Unfortunately, most existing methods fail to take advantage of the spatial information in data. To overcome this limitation, we propose a Structured Sparse regularized Nonnegative Matrix Factorization (SS-NMF) method based on the following two aspects. First, we incorporate a graph Laplacian to encode the manifold structures embedded in the hyperspectral data space. In this way, the highly similar neighboring pixels can be grouped together. Second, the lasso penalty is employed in SS-NMF for the fact that pixels in the same manifold structure are sparsely mixed by a common set of relevant bases. These two factors act as a new structured sparse constraint. With this constraint, our method can learn a compact space, where highly similar pixels are grouped to share correlated sparse representations. Experiments on real hyperspectral data sets with different noise levels demonstrate that our method outperforms the state-of-the-art methods significantly. (C) 2013 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.
KeywordHyperspectral Unmixing (Hu) Hyperspectral Image Analysis Structured Sparse Nmf (ss-Nmf) Mixed Pixel Nonnegative Matrix Factorization (Nmf)
WOS HeadingsScience & Technology ; Physical Sciences ; Technology
WOS KeywordNONNEGATIVE MATRIX FACTORIZATION ; ENDMEMBER EXTRACTION ALGORITHM ; CONSTRAINED LEAST-SQUARES ; COMPONENT ANALYSIS ; IMAGERY ; QUANTIFICATION ; REPRESENTATION ; GRADIENT ; PARTS
Indexed BySCI
Language英语
WOS Research AreaPhysical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000331921200010
Citation statistics
Cited Times:97[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/3714
Collection模式识别国家重点实验室_先进数据分析与学习
AffiliationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100864, Peoples R China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Zhu, Feiyun,Wang, Ying,Xiang, Shiming,et al. Structured Sparse Method for Hyperspectral Unmixing[J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,2014,88:101-118.
APA Zhu, Feiyun,Wang, Ying,Xiang, Shiming,Fan, Bin,&Pan, Chunhong.(2014).Structured Sparse Method for Hyperspectral Unmixing.ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING,88,101-118.
MLA Zhu, Feiyun,et al."Structured Sparse Method for Hyperspectral Unmixing".ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 88(2014):101-118.
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