Large kernel spectral and spatial attention networks for hyperspectral image classification.

Sun, Genyun; Pan, Zhaojie; Zhang, Aizhu; Jia, Xiuping; Ren, Jinchang; Fu, Hang; Yan, Kai

Abstract

Currently, long-range spectral and spatial dependencies have been widely demonstrated to be essential for hyperspectral image (HSI) classification. Due to the transformer superior ability to exploit long-range representations, the transformer-based methods have exhibited enormous potential. However, existing transformer-based approaches still face two crucial issues that hinder the further performance promotion of HSI classification: 1) treating HSI as 1D sequences neglects spatial properties of HSI, 2) the dependence between spectral and spatial information is not fully considered. To tackle the above problems, a large kernel spectral-spatial attention network (LKSSAN) is proposed to capture the long-range 3D properties of HSI, which is inspired by the visual attention network (VAN). Specifically, a spectral-spatial attention module is first proposed to effectively exploit discriminative 3D spectral-spatial features while keeping the 3D structure of HSI. This module introduces the large kernel attention (LKA) and convolution feed-forward (CFF) to flexibly emphasize, model, and exploit the long-range 3D feature dependencies with lower computational pressure. Finally, the features from the spectral-spatial attention module are fed into the classification module for the optimization of 3D spectral-spatial representation. To verify the effectiveness of the proposed classification method, experiments are executed on four widely used HSI data sets. The experiments demonstrate that LKSSAN is indeed an effective way for long-range 3D feature extraction of HSI.

Citation

SUN, G., PAN, Z., ZHANG, A., JIA, X., REN, J., FU, H. and YAN, K. 2023. Large kernel spectral and spatial attention networks for hyperspectral image classification. IEEE transactions on geoscience and remote sensing [online], 61, article 5519915. Available from: https://doi.org/10.1109/tgrs.2023.3292065

• Journal Article Type: Article
• Acceptance Date: Jun 25, 2023
• Online Publication Date: Jul 10, 2023
• Publication Date: Dec 31, 2023
• Deposit Date: Jul 24, 2023
• Publicly Available Date: Jul 24, 2023
• 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: 61
• Article Number: 5519915
• DOI: https://doi.org/10.1109/tgrs.2023.3292065
• Keywords: Deep learning; Long-range 3D spectral-spatial feature extraction; Spectral-spatial attention; Large kernel attention (LKA); Convolutional feed-forward (CFF); Hyperspectral image (HSI) classification
• Public URL: https://rgu-repository.worktribe.com/output/2010443