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Spectral-spatial self-attention networks for hyperspectral image classification.

Zhang, Xuming; Sun, Genyun; Jia, Xiuping; Wu, Lixin; Zhang, Aizhu; Ren, Jinchang; Fu, Hang; Yao, Yanjuan

Authors

Xuming Zhang

Genyun Sun

Xiuping Jia

Lixin Wu

Aizhu Zhang

Hang Fu

Yanjuan Yao



Abstract

This study presents a spectral-spatial self-attention network (SSSAN) for classification of hyperspectral images (HSIs), which can adaptively integrate local features with long-range dependencies related to the pixel to be classified. Specifically, it has two subnetworks. The spatial subnetwork introduces the proposed spatial self-attention module to exploit rich patch-based contextual information related to the center pixel. The spectral subnetwork introduces the proposed spectral self-attention module to exploit the long-range spectral correlation over local spectral features. The extracted spectral and spatial features are then adaptively fused for HSI classification. Experiments conducted on four HSI datasets demonstrate that the proposed network outperforms several state-of-the-art methods.

Citation

ZHANG, X., SUN, G., JIA, X., WU, L., ZHANG, A., REN, J., FU, H. and YAO, Y. 2022. Spectral-spatial self-attention networks for hyperspectral image classification. IEEE transactions on geoscience and remote sensing [online], 60, article 5512115. Available from: https://doi.org/10.1109/TGRS.2021.3102143

Journal Article Type Article
Acceptance Date Jul 4, 2021
Online Publication Date Aug 6, 2021
Publication Date Jan 17, 2022
Deposit Date Aug 13, 2021
Publicly Available Date Aug 13, 2021
Journal IEEE Transactions on Geoscience and Remote Sensing
Print ISSN 0196-2892
Electronic ISSN 1558-0644
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Peer Reviewed Peer Reviewed
Volume 60
Article Number 5512115
DOI https://doi.org/10.1109/TGRS.2021.3102143
Keywords Adaptation models; Convolution; Convolutional neural network (CNN); Correlation; Deep learning; Feature extraction; Hyperspectral image (HSI) classification; Hyperspectral imaging; Image segmentation; Spatial self-attention module; Spectral self-attention
Public URL https://rgu-repository.worktribe.com/output/1406215

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ZHANG 2022 Spectral spatial self (AAM) (38.4 Mb)
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