Hyperspectral Band Selection With Iterative Graph Autoencoder

Hyperspectral band selection (BS) is an important task for hyperspectral image (HSI) processing, which aims to select a discriminative and low-redundant band subset. As a significant cue for BS, structure information describes the cross-band correlation, which brings the redundancy of HSI. Existing...

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Vydané v:	IEEE transactions on geoscience and remote sensing Ročník 61; s. 1 - 13
Hlavní autori:	Zhou, Yuan, Yao, Qingren, Huo, Shuwei, Li, Xiaofeng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Artificial neural networks Banded structure Construction Convolution Correlation Design Feature extraction Graph autoencoder (GAE) Graph neural networks graph representation Graphical representations hyperspectral band selection (BS) Hyperspectral imaging Information processing Iterative methods Methods Neural networks Redundancy representativeness Symmetric matrices Task analysis
ISSN:	0196-2892, 1558-0644
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