Sparsity-Enhanced Convolutional Decomposition: A Novel Tensor-Based Paradigm for Blind Hyperspectral Unmixing

Blind hyperspectral unmixing (HU) has long been recognized as a crucial component in analyzing the hyperspectral imagery (HSI) collected by airborne and spaceborne sensors. Due to the highly ill-posed problems of such a blind source separation scheme and the effects of spectral variability in hypers...

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Vydané v:	IEEE transactions on geoscience and remote sensing Ročník 60; s. 1 - 14
Hlavní autori:	Yao, Jing, Hong, Danfeng, Xu, Lin, Meng, Deyu, Chanussot, Jocelyn, Xu, Zongben
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Predmet:	Airborne sensing Algorithms Blind hyperspectral unmixing (HU) Computer Science Context modeling Convolutional codes convolutional sparse coding (CSC) Decomposition Encoding Hyperspectral imaging Ill posed problems Imagery Mathematical analysis Optimization Regularization Signal and Image Processing Signal processing Sparsity Spectra spectral bundles spectral variability (SV) Task analysis tensor decomposition Tensors Convolutional codes Tensors Encoding Task analysis Optimization Hyperspectral imaging Context modeling
ISSN:	0196-2892, 1558-0644
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