Matrix-Vector Nonnegative Tensor Factorization for Blind Unmixing of Hyperspectral Imagery

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 stack...

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Bibliographic Details
Published in:	IEEE transactions on geoscience and remote sensing Vol. 55; no. 3; pp. 1776 - 1792
Main Authors:	Qian, Yuntao, Xiong, Fengchao, Zeng, Shan, Zhou, Jun, Tang, Yuan Yan
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.03.2017 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Data processing Decomposition Distance measurement Factorization Feature extraction Hyperspectral imagery (HSI) Hyperspectral imaging Imagery Mathematical analysis Mathematical models Matrix algebra Matrix decomposition Matrix methods Mixture models Pixels Spatial data Spectra spectral unmixing spectral-spatial structure State of the art Statistical methods Tensile stress tensor factorization Tensors
ISSN:	0196-2892, 1558-0644
Online Access:	Get full text
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