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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Bibliographic Details
Published in:	IEEE transactions on geoscience and remote sensing Vol. 60; pp. 1 - 15
Main Authors:	Jin, Qiwen, Ma, Yong, Mei, Xiaoguang, Ma, Jiayi
Format:	Journal Article
Language:	English
Published:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	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
Online Access:	Get full text
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