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dc.contributor.authorQian, Yuntao
dc.contributor.authorXiong, Fengchao
dc.contributor.authorZeng, Shan
dc.contributor.authorZhou, Jun
dc.contributor.authorTang, Yuan Yan
dc.date.accessioned2017-08-04T12:30:53Z
dc.date.available2017-08-04T12:30:53Z
dc.date.issued2017
dc.identifier.issn0196-2892
dc.identifier.doi10.1109/TGRS.2016.2633279
dc.identifier.urihttp://hdl.handle.net/10072/340636
dc.description.abstractMany spectral unmixing approaches ranging from geometry, algebra to statistics have been proposed, in which nonnegative matrix factorization (NMF)-based ones form an important family. The original NMF-based unmixing algorithm loses the spectral and spatial information between mixed pixels when stacking the spectral responses of the pixels into an observed matrix. Therefore, various constrained NMF methods are developed to impose spectral structure, spatial structure, and spectral-spatial joint structure into NMF to enforce the estimated endmembers and abundances preserve these structures. Compared with matrix format, the third-order tensor is more natural to represent a hyperspectral data cube as a whole, by which the intrinsic structure of hyperspectral imagery can be losslessly retained. Extended from NMF-based methods, a matrix-vector nonnegative tensor factorization (NTF) model is proposed in this paper for spectral unmixing. Different from widely used tensor factorization models, such as canonical polyadic decomposition CPD) and Tucker decomposition, the proposed method is derived from block term decomposition, which is a combination of CPD and Tucker decomposition. This leads to a more flexible frame to model various application-dependent problems. The matrix-vector NTF decomposes a third-order tensor into the sum of several component tensors, with each component tensor being the outer product of a vector (endmember) and a matrix (corresponding abundances). From a formal perspective, this tensor decomposition is consistent with linear spectral mixture model. From an informative perspective, the structures within spatial domain, within spectral domain, and cross spectral-spatial domain are retreated interdependently. Experiments demonstrate that the proposed method has outperformed several state-of-the-art NMF-based unmixing methods.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherInstitute of Electircal and Electronics Engineers
dc.relation.ispartofpagefrom1776
dc.relation.ispartofpageto1792
dc.relation.ispartofissue3
dc.relation.ispartofjournalIEEE Transactions on Geoscience and Remote Sensing
dc.relation.ispartofvolume55
dc.subject.fieldofresearchElectrical and Electronic Engineering not elsewhere classified
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchGeophysics
dc.subject.fieldofresearchElectrical and Electronic Engineering
dc.subject.fieldofresearchGeomatic Engineering
dc.subject.fieldofresearchcode090699
dc.subject.fieldofresearchcode0801
dc.subject.fieldofresearchcode0404
dc.subject.fieldofresearchcode0906
dc.subject.fieldofresearchcode0909
dc.titleMatrix-Vector Nonnegative Tensor Factorization for Blind Unmixing of Hyperspectral Imagery
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2017 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.
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gro.griffith.authorZhou, Jun


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