Graph Attention Convolutional Autoencoder-Based Unsupervised Nonlinear Unmixing for Hyperspectral Images

Hyperspectral unmixing has received increasing attention as a technique for estimating endmember spectra and fractional abundances of land covers. Encoding high-dimensional hyperspectral data into a low-dimensional latent space to generate reasonable abundances, autoencoder (AE) has shown its great...

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Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing Vol. 16; pp. 1 - 11
Main Authors: Jin, Danni, Yang, Bin
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Accuracy
Autoencoder
bilinear mixture model
Convolution
Convolutional neural networks
Decoding
Feature extraction
graph attention convolutional network
Hyperspectral imaging
hyperspectral remote sensing imagery
Mathematical models
Mixing
Mixture models
nonlinear spectral unmixing
Spatial data
Spectra
Synthetic data
ISSN:1939-1404, 2151-1535
Online Access:Get full text
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Summary:Hyperspectral unmixing has received increasing attention as a technique for estimating endmember spectra and fractional abundances of land covers. Encoding high-dimensional hyperspectral data into a low-dimensional latent space to generate reasonable abundances, autoencoder (AE) has shown its great potential and attractive advantages in spectral unmixing. AEs decode abundances back to spectra, which can effectively reflect the general spectral mixing process. However, most existing AE-based unmixing methods often do not fully exploit the spatial information of hyperspectral images, hindering the improvement of unmixing accuracy. This paper proposes a graph attention convolutional autoencoder architecture for hyperspectral unmixing. By incorporating graph attention convolution into AE, the proposed method performs better in leveraging both long-range and short-range spatial information of hyperspectral images. Accurate abundances with global and local spatial consistency can be efficiently learned by the network. Moreover, the decoder is further improved based on the post-polynomial nonlinear mixing (PPNM) model to make the network have stronger physical interpretability to deal with the issue of nonlinear blind unmixing. Experimental results indicate that the proposed method has good unmixing performance. It can reduce the root mean squared error of estimated abundances for synthetic data by over 10% compared to other methods. In experiments with real hyperspectral data, the difference between its unmixing results' accuracy and the best is less than 5%.
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ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2023.3308037