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dc.contributor.authorTong, Lei
dc.contributor.authorZhou, Jun
dc.contributor.authorLi, Xue
dc.contributor.authorQian, Yuntao
dc.contributor.authorGao, Yongsheng
dc.date.accessioned2017-08-04T12:30:51Z
dc.date.available2017-08-04T12:30:51Z
dc.date.issued2017
dc.identifier.issn1939-1404
dc.identifier.doi10.1109/JSTARS.2016.2621003
dc.identifier.urihttp://hdl.handle.net/10072/340633
dc.description.abstractHyperspectral unmixing is one of the most important techniques in the remote sensing image analysis. In recent years, the nonnegative matrix factorization (NMF) method is widely used in hyperspectral unmixing. In order to solve the nonconvex problem of the NMF method, a number of constraints have been introduced into NMF models, including sparsity, manifold, smoothness, etc. However, these constraints ignore an important property of a hyperspectral image, i.e., the spectral responses in a homogeneous region are similar at each pixel but vary in different homogeneous regions. In this paper, we introduce a novel region-based structure preserving NMF (R-NMF) to explore consistent data distribution in the same region while discriminating different data structures across regions in the unmixed data. In this method, a graph cut algorithm is first applied to segment the hyperspectral image to small homogeneous regions. Then, two constraints are applied to the unmixing model, which preserve the structural consistency within the region while discriminating the differences between regions. Results on both synthetic and real data have validated the effectiveness of this method, and shown that it has outperformed several state-of-the-art unmixing approaches.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartofpagefrom1575
dc.relation.ispartofpageto1588
dc.relation.ispartofissue4
dc.relation.ispartofjournalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
dc.relation.ispartofvolume10
dc.subject.fieldofresearchPhotogrammetry and Remote Sensing
dc.subject.fieldofresearchPhysical Geography and Environmental Geoscience
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchGeomatic Engineering
dc.subject.fieldofresearchcode090905
dc.subject.fieldofresearchcode0406
dc.subject.fieldofresearchcode0801
dc.subject.fieldofresearchcode0909
dc.titleRegion-Based Structure Preserving Nonnegative Matrix Factorization for Hyperspectral Unmixing
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.hasfulltextFull Text
gro.griffith.authorGao, Yongsheng
gro.griffith.authorZhou, Jun


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