Translator Disclaimer
14 February 2020 Spectral group attention networks for hyperspectral image classification
Author Affiliations +
Proceedings Volume 11428, MIPPR 2019: Multispectral Image Acquisition, Processing, and Analysis; 114280H (2020)
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China
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.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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);

Back to Top