TANet: An Unsupervised Two-Stream Autoencoder Network for Hyperspectral Unmixing

Spectral unmixing is a major technique for the further development of hyperspectral analysis. It aims to determine the corresponding proportion (fractional abundance) of the basic spectral signatures (endmembers) blindly at the subpixel level. Recently, the learning-based method has received much at...

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Vydané v:IEEE transactions on geoscience and remote sensing Ročník 60; s. 1 - 15
Hlavní autori: Jin, Qiwen, Ma, Yong, Mei, Xiaoguang, Ma, Jiayi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Autoencoder
Data mining
Decoding
deep learning
hyperspectral image (HSI)
Hyperspectral imaging
hyperspectral unmixing
Image reconstruction
Image segmentation
Learning
Neural networks
Pixels
Rivers
Spectral signatures
Task analysis
Training
ISSN:0196-2892, 1558-0644
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Shrnutí:Spectral unmixing is a major technique for the further development of hyperspectral analysis. It aims to determine the corresponding proportion (fractional abundance) of the basic spectral signatures (endmembers) blindly at the subpixel level. Recently, the learning-based method has received much attention in hyperspectral unmixing, and autoencoders have been effectively designed to solve the unsupervised scenarios of unmixing. However, their ability to extract physically meaningful endmembers remains limited, and the performance has not been satisfactory. In this article, we propose a novel two-stream network, termed TANet, to address the above problems. The network consists of a two-stream architecture. First, superpixel segmentation is adopted as preprocessing to extract the endmember bundles from the image. Then, the first stream learns a mapping from the pseudopure pixels to their corresponding abundances. The second stream is conducting the same untied-weighted autoencoder to minimize reconstruction errors from the original pixel data. By learning from the pure or nearly pure candidate pixels to correct the weights of unmixing, the proposed TANet exhibits a more accurate and interpretable unmixing performance. Extensive experiments on both synthetic and real hyperspectral data demonstrate that the proposed TANet can outperform the other state-of-the-art approaches.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2021.3094884