Hyperspectral Unmixing for Additive Nonlinear Models With a 3-D-CNN Autoencoder Network

Spectral unmixing is an important task in hyperspectral image processing for separating the mixed spectral data pertaining to various materials observed aiming at analyzing the material components in observed pixels. Recently, nonlinear spectral unmixing has received particular attention in hyperspe...

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Vydané v:	IEEE transactions on geoscience and remote sensing Ročník 60; s. 1 - 15
Hlavní autori:	Zhao, Min, Wang, Mou, Chen, Jie, Rahardja, Susanto
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	3-D-convolutional neural network (CNN) Additives Artificial neural networks autoencoder network Decoding Hyperspectral imaging Image processing Kernel Mixture models Neural networks nonlinear spectral unmixing Nonlinear systems Nonlinearity Photonics Spectra
ISSN:	0196-2892, 1558-0644
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