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Matrix-Vector Nonnegative Tensor Factorization for Blind Unmixing of Hyperspectral Imagery
Qian, Yuntao1; Xiong, Fengchao1; Zeng, Shan2; Zhou, Jun3; Tang, Yuan Yan4
2017-03
Source PublicationIEEE Transactions on Geoscience and Remote Sensing
ISSN0196-2892
Volume55Issue:3Pages:1776-1792
Abstract

Many 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-theart NMF-based unmixing methods.

KeywordHyperspectral Imagery (Hsi) Spectral Unmixing Spectral-spatial Structure Tensor Factorization
DOIhttps://doi.org/10.1109/TGRS.2016.2633279
URLView the original
Indexed BySCI
Language英语
WOS Research AreaGeochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectGeochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000396106700046
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
The Source to ArticleWOS
Fulltext Access
Citation statistics
Cited Times [WOS]:23   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorQian, Yuntao
Affiliation1.College of Computer Science, Institute of Artificial Intelligence, Zhejiang University, Hangzhou 310027, China
2.College of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan 430023, China
3.School of Information and Communication Technology, Griffith University, Nathan 4111, Australia
4.Faculty of Science and Technology, University of Macau, Macau 999078, China.
Recommended Citation
GB/T 7714
Qian, Yuntao,Xiong, Fengchao,Zeng, Shan,et al. Matrix-Vector Nonnegative Tensor Factorization for Blind Unmixing of Hyperspectral Imagery[J]. IEEE Transactions on Geoscience and Remote Sensing,2017,55(3):1776-1792.
APA Qian, Yuntao,Xiong, Fengchao,Zeng, Shan,Zhou, Jun,&Tang, Yuan Yan.(2017).Matrix-Vector Nonnegative Tensor Factorization for Blind Unmixing of Hyperspectral Imagery.IEEE Transactions on Geoscience and Remote Sensing,55(3),1776-1792.
MLA Qian, Yuntao,et al."Matrix-Vector Nonnegative Tensor Factorization for Blind Unmixing of Hyperspectral Imagery".IEEE Transactions on Geoscience and Remote Sensing 55.3(2017):1776-1792.
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