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...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	IEEE transactions on geoscience and remote sensing Jg. 60; S. 1 - 15
Hauptverfasser:	Zhao, Min, Wang, Mou, Chen, Jie, Rahardja, Susanto
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!