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Manifold Regularized Sparse NMF for Hyperspectral Unmixing
Lu, Xiaoqiang1; Wu, Hao2; Yuan, Yuan1; Yan, Pingkun1; Li, Xuelong1
2013-05-01
Source PublicationIEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume51Issue:5Pages:2815-2826
AbstractHyperspectral unmixing is one of the most important techniques in analyzing hyperspectral images, which decomposes a mixed pixel into a collection of constituent materials weighted by their proportions. Recently, many sparse nonnegative matrix factorization (NMF) algorithms have achieved advanced performance for hyperspectral unmixing because they overcome the difficulty of absence of pure pixels and sufficiently utilize the sparse characteristic of the data. However, most existing sparse NMF algorithms for hyperspectral unmixing only consider the Euclidean structure of the hyperspectral data space. In fact, hyperspectral data are more likely to lie on a low-dimensional submanifold embedded in the high-dimensional ambient space. Thus, it is necessary to consider the intrinsic manifold structure for hyperspectral unmixing. In order to exploit the latent manifold structure of the data during the decomposition, manifold regularization is incorporated into sparsity-constrained NMF for unmixing in this paper. Since the additional manifold regularization term can keep the close link between the original image and the material abundance maps, the proposed approach leads to a more desired unmixing performance. The experimental results on synthetic and real hyperspectral data both illustrate the superiority of the proposed method compared with other state-of-the-art approaches.
SubtypeArticle
KeywordHyperspectral Unmixing Manifold Regularization Mixed Pixel Nonnegative Matrix Factorization (Nmf)
WOS HeadingsScience & Technology ; Physical Sciences ; Technology
DOI10.1109/TGRS.2012.2213825
Indexed BySCI ; EI
WOS KeywordNONNEGATIVE MATRIX FACTORIZATION ; ENDMEMBER EXTRACTION ; COMPONENT ANALYSIS ; ALGORITHM ; IMAGERY
Language英语
WOS Research AreaGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000318428700025
Citation statistics
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/24009
Collection光学影像学习与分析中心
Affiliation1.Chinese Acad Sci, Ctr Opt Imagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Peoples R China
2.Hubei Univ, Fac Math & Comp Sci, Wuhan 430062, Peoples R China
Recommended Citation
GB/T 7714
Lu, Xiaoqiang,Wu, Hao,Yuan, Yuan,et al. Manifold Regularized Sparse NMF for Hyperspectral Unmixing[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2013,51(5):2815-2826.
APA Lu, Xiaoqiang,Wu, Hao,Yuan, Yuan,Yan, Pingkun,&Li, Xuelong.(2013).Manifold Regularized Sparse NMF for Hyperspectral Unmixing.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,51(5),2815-2826.
MLA Lu, Xiaoqiang,et al."Manifold Regularized Sparse NMF for Hyperspectral Unmixing".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 51.5(2013):2815-2826.
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