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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Bibliographic Details

Published in:	IEEE transactions on geoscience and remote sensing Vol. 60; pp. 1 - 15
Main Authors:	Zhao, Min, Wang, Mou, Chen, Jie, Rahardja, Susanto
Format:	Journal Article
Language:	English
Published:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
Online Access:	Get full text
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