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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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
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Zusammenfassung: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 hyperspectral image processing, as there are many situations in which the linear mixture model may not be appropriate and could be advantageously replaced by a nonlinear one. Existing nonlinear unmixing approaches are often based on specific assumptions on the nonlinearity and can be less effective when used for scenes with unknown nonlinearity. This article presents an unsupervised nonlinear spectral unmixing method that addresses a general model that consists of a linear mixture part and an additive nonlinear mixture part. The structure of a deep autoencoder network, which has a clear physical interpretation, is specifically designed to achieve this purpose. Moreover, a convolutional neural network (CNN) is used to capture the spectral-spatial priors from hyperspectral data. Extensive experiments with synthetic and real data illustrate the generality and effectiveness of this scheme compared with state-of-the-art methods.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2021.3098745