Spectral group attention networks for hyperspectral image classification

Zhengtao Li; Zhongyang Wang; Hai Xu; Yaozong Zhang; Tianxu Zhang

doi:10.1117/12.2537756

14 February 2020 Spectral group attention networks for hyperspectral image classification

Zhengtao Li, Zhongyang Wang, Hai Xu, Yaozong Zhang, Tianxu Zhang

Proceedings Volume 11428, MIPPR 2019: Multispectral Image Acquisition, Processing, and Analysis; 114280H (2020) https://doi.org/10.1117/12.2537756
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

Abstract

Attention mechanism in deep learning is similar to information selection mechanism, and the goal of attention is to select critical information for the current task. In hyperspectral classification, the distinction of some categories depends on the subtle differences, however, most of the classification methods have the problem of insufficient expression ability to discriminate the fine differences of categories. In this paper, a classification method based on group attention is proposed to enhance the difference of hyperspectral data between categories. Firstly, we slice the hyperspectral sample into several groups on spectral channels, and extract the group CNN features. Then we use the attention module to obtain the attention weights for each spectral group. Finally, the "feature recalibration" strategy is used to recalibrate the spectral group CNN features. The experiment show that the proposed approach can improve the classification accuracy of categories with subtle differences.

Citation Download Citation

Zhengtao Li, Zhongyang Wang, Hai Xu, Yaozong Zhang, and Tianxu Zhang "Spectral group attention networks for hyperspectral image classification", Proc. SPIE 11428, MIPPR 2019: Multispectral Image Acquisition, Processing, and Analysis, 114280H (14 February 2020); https://doi.org/10.1117/12.2537756

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KEYWORDS

Image classification

Hyperspectral imaging

Feature extraction

Neural networks

Convolution

Data modeling

Spectroscopy

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