Structured Sparse Method for Hyperspectral Unmixing

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 先进时空数据分析与学习

	Structured Sparse Method for Hyperspectral Unmixing
	Zhu, Feiyun; Wang, Ying; Xiang, Shiming; Fan, Bin; Pan, Chunhong
发表期刊	ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
	2014-02-01
卷号	88 页码:101-118
文章类型	Article
摘要	Hyperspectral 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.
关键词	Hyperspectral Unmixing (Hu) Hyperspectral Image Analysis Structured Sparse Nmf (ss-Nmf) Mixed Pixel Nonnegative Matrix Factorization (Nmf)
WOS标题词	Science & Technology ; Physical Sciences ; Technology
关键词[WOS]	NONNEGATIVE MATRIX FACTORIZATION ; ENDMEMBER EXTRACTION ALGORITHM ; CONSTRAINED LEAST-SQUARES ; COMPONENT ANALYSIS ; IMAGERY ; QUANTIFICATION ; REPRESENTATION ; GRADIENT ; PARTS
收录类别	SCI
语种	英语
WOS研究方向	Physical Geography ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geography, Physical ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:000331921200010
引用统计	被引频次：187[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3714
专题	多模态人工智能系统全国重点实验室_先进时空数据分析与学习
作者单位	Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100864, Peoples R China
第一作者单位	模式识别国家重点实验室
推荐引用方式 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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