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dc.contributor.authorQian, Binen_US
dc.contributor.authorZhou, Junen_US
dc.contributor.authorTong, Leien_US
dc.contributor.authorShen, Xiaoboen_US
dc.contributor.authorLiu, Fanen_US
dc.contributor.editorPatrick Le Callet, Baoxin Lien_US
dc.date.accessioned2017-05-29T12:32:49Z
dc.date.available2017-05-29T12:32:49Z
dc.date.issued2016en_US
dc.identifier.doi10.1109/ICIP.2016.7532677en_US
dc.identifier.urihttp://hdl.handle.net/10072/124193
dc.description.abstractNonnegative matrix factorization (NMF) based hyperspectral unmixing aims at estimating pure spectral signatures and their fractional abundances at each pixel. During the past several years, manifold structures have been introduced as regularization constraints into NMF. However, most methods only consider the constraints on abundance matrix while ignoring the geometric relationship of endmembers. Although such relationship can be described by traditional graph construction approaches based on k-nearest neighbors, its accuracy is questionable. In this paper, we propose a novel hyperspectral unmixing method, namely NMF with endmember sparse graph learning, to tackle the above drawbacks. This method first integrates endmember sparse graph structure into NMF, then simultaneously performs unmixing and graph learning. It is further extended by incorporating abundance smoothness constraint to improve the unmixing performance. Experimental results on both synthetic and real datasets have validated the effectiveness of the proposed method.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.publisher.placeUnited Statesen_US
dc.relation.ispartofconferencenameICIP 2016en_US
dc.relation.ispartofconferencetitle2016 IEEE International Conference on Image Processing: Proceedingsen_US
dc.relation.ispartofdatefrom2016-09-25en_US
dc.relation.ispartofdateto2016-09-28en_US
dc.relation.ispartoflocationPhoenix, Arizona, United Statesen_US
dc.subject.fieldofresearchImage Processingen_US
dc.subject.fieldofresearchcode080106en_US
dc.titleNonnegative matrix factorization with endmember sparse graph learning for hyperspectral unmixingen_US
dc.typeConference outputen_US
dc.type.descriptionE1 - Conference Publications (HERDC)en_US
dc.type.codeE - Conference Publicationsen_US
dc.description.versionPost-printen_US
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.en_US
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