Deep Autoencoder for Hyperspectral Unmixing via Global-Local Smoothing

Hyperspectral unmixing is to decompose the mixed pixels into pure spectral signatures (endmembers) and their proportions (abundances). Recently, deep learning-based methods have been applied to enhance the representation ability of unmixing models by extracting joint spatial-spectral characteristics...

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Veröffentlicht in:IEEE transactions on geoscience and remote sensing Jg. 60; S. 1 - 16
Hauptverfasser: Xu, Xia, Song, Xinyu, Li, Tao, Shi, Zhenwei, Pan, Bin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Abundance
Autoencoder (AE)
Conditional random fields
Convolution
Data mining
Deep learning
Feature extraction
global and local
Homogeneity
Hyperspectral imaging
hyperspectral unmixing
Imagery
Methods
Modules
Neural networks
Pixels
Recurrent neural networks
Risk reduction
Self-similarity
Smoothing
Smoothing methods
Spectra
Spectral signatures
Task analysis
ISSN:0196-2892, 1558-0644
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Zusammenfassung:Hyperspectral unmixing is to decompose the mixed pixels into pure spectral signatures (endmembers) and their proportions (abundances). Recently, deep learning-based methods have been applied to enhance the representation ability of unmixing models by extracting joint spatial-spectral characteristics of the hyperspectral data. However, most deep learning based-unmixing methods usually conduct global smoothing by convolutions on the whole hyperspectral imagery, which may ignore the variations within the imagery and result in oversmoothing. In this article, we propose a deep network for hyperspectral unmixing based on a new global-local smoothing autoencoder (GLA). GLA is an unsupervised model, which aims at exploring the local homogeneity and the global self-similarity of hyperspectral imagery. The proposed GLA network mainly includes two modules: a Local Continuous conditional random field Smoothing (LCS) module and a global recurrent smoothing (GRS) module. In LCS, we propose a conditional random field-based smoothing strategy to describe the joint spatial-spectral information within a local homogeneity region, which also reduces the risk of abundance maps boundary blurry. In GRS, we follow the self-similarity assumption for hyperspectral imagery and develop a recurrent neural network structure to exploit potential long-distance dependency relationships among pixels. The GLA is compared with several state-of-the-art unmixing methods on both real and synthetic data, and the abundance estimation results indicate that our method is promising. We will publish the code of GLA if this article has the honor to be accepted.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2022.3152782