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dc.contributor.authorTong, Lei
dc.contributor.authorZhou, Jun
dc.contributor.authorQian, Yuntao
dc.contributor.authorBai, Xiao
dc.contributor.authorGao, Yongsheng
dc.date.accessioned2017-05-22T05:37:27Z
dc.date.available2017-05-22T05:37:27Z
dc.date.issued2016
dc.identifier.issn0196-2892
dc.identifier.doi10.1109/TGRS.2016.2586110
dc.identifier.urihttp://hdl.handle.net/10072/100457
dc.description.abstractHyperspectral unmixing is an important technique for estimating fractions of various materials from remote sensing imagery. Most unmixing methods make the assumption that no prior knowledge of endmembers is available before the estimation. This is, however, not true for some unmixing tasks for which part of the endmember signatures may be known in advance. In this paper, we address the hyperspectral unmixing problem with partially known endmembers. We extend nonnegative-matrix-factorization-based unmixing algorithms to incorporate prior information into their models. The proposed approach uses the spectral signature of known endmembers as a constraint, among others, in the unmixing model, and propagates the knowledge by an optimization process which minimizes the difference between the image data and the prior knowledge. Results on both synthetic and real data have validated the effectiveness of the proposed method and have shown that it has outperformed several state-of-the-art methods that use or do not use prior knowledge of endmembers.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherInstitute of Electircal and Electronics Engineers
dc.relation.ispartofpagefrom6531
dc.relation.ispartofpageto6544
dc.relation.ispartofissue11
dc.relation.ispartofjournalIEEE Transactions on Geoscience and Remote Sensing
dc.relation.ispartofvolume54
dc.subject.fieldofresearchGeophysics
dc.subject.fieldofresearchGeomatic engineering
dc.subject.fieldofresearchEarth sciences
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchcode3706
dc.subject.fieldofresearchcode4013
dc.subject.fieldofresearchcode37
dc.subject.fieldofresearchcode40
dc.titleNonnegative-Matrix-Factorization-Based Hyperspectral Unmixing With Partially Known Endmembers
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dc.description.versionAccepted Manuscript (AM)
gro.facultyGriffith Sciences, Griffith School of Engineering
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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