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dc.contributor.authorQian, Bin
dc.contributor.authorZhou, Jun
dc.contributor.authorTong, Lei
dc.contributor.authorShen, Xiaobo
dc.contributor.authorLiu, Fan
dc.contributor.editorPatrick Le Callet, Baoxin Li
dc.date.accessioned2017-05-29T12:32:49Z
dc.date.available2017-05-29T12:32:49Z
dc.date.issued2016
dc.identifier.isbn9781467399616
dc.identifier.issn1522-4880
dc.identifier.doi10.1109/ICIP.2016.7532677
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.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.placeUnited States
dc.relation.ispartofconferencename23rd IEEE International Conference on Image Processing (ICIP)
dc.relation.ispartofconferencetitle2016 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP)
dc.relation.ispartofdatefrom2016-09-25
dc.relation.ispartofdateto2016-09-28
dc.relation.ispartoflocationPhoenix, AZ
dc.relation.ispartofpagefrom1843
dc.relation.ispartofpageto1847
dc.relation.ispartofvolume2016-August
dc.subject.fieldofresearchImage Processing
dc.subject.fieldofresearchcode080106
dc.titleNonnegative matrix factorization with endmember sparse graph learning for hyperspectral unmixing
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dc.description.versionPost-print
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.authorZhou, Jun
gro.griffith.authorTong, Lei


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