Hyperspectral image classification based on parallel-branch expectation-maximization attention mechanism

Hao Wen; Yuanxi Peng; Xiang Hu

doi:10.1117/12.2604583

5 October 2021 Hyperspectral image classification based on parallel-branch expectation-maximization attention mechanism

Hao Wen, Yuanxi Peng, Xiang Hu

Proceedings Volume 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning; 119110M (2021) https://doi.org/10.1117/12.2604583
Event: 2nd International Conference on Computer Vision, Image and Deep Learning, 2021, Liuzhou, China

Abstract

Hyperspectral images (HSI) cover a very large area, how to achieve excellent classification performance with limited time consumptions is still a challenging issue. To reduce running time and improve accuracy, a parallel-branch expectation-maximization (PBEM) attention principle method will be proposed to HSIs classification in this article. In my cognition, this may be the first study to apply the expectation-maximization attention methodology in hyperspectral image classification. Besides, we believe we are the first to combine the disout layer and the expectation-maximization attention methodology in hyperspectral image classification. The experimental results from benchmark dataset prove the superiority of our team proposed methodology in hyperspectral image classification, especially in small sample classification task.

Citation Download Citation

Hao Wen, Yuanxi Peng, and Xiang Hu "Hyperspectral image classification based on parallel-branch expectation-maximization attention mechanism", Proc. SPIE 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning, 119110M (5 October 2021); https://doi.org/10.1117/12.2604583

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KEYWORDS

Image classification

Hyperspectral imaging

Feature extraction

Performance modeling

Convolution

Expectation maximization algorithms

Process modeling

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