Classification of hyperspectral image based on multi-scale convolutional neural network and attention mechanism

Chang Liu; Han Jiang; Yuhui Shi; Panpan Xun; Renhao Liu

doi:10.1117/12.2604557

5 October 2021 Classification of hyperspectral image based on multi-scale convolutional neural network and attention mechanism

Chang Liu, Han Jiang, Yuhui Shi, Panpan Xun, Renhao Liu

Author Affiliations +

Proceedings Volume 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning; 1191117 (2021) https://doi.org/10.1117/12.2604557
Event: 2nd International Conference on Computer Vision, Image and Deep Learning, 2021, Liuzhou, China

Abstract

With the development of hyperspectral technology and the increase of hyperspectral dimension, single model is difficult to apply to the process of feature selection, feature extraction and feature integration for hyperspectral image, causing the undesirable hyperspectral classification effect. In order to improve the classification accuracy, a kind of algorithm of uniting convolutional neural network and multihead attention is proposed. Firstly, PCA algorithm is used for dimensionality reduction of hyperspectral data; Then, excavation feature of multi-scale convolutional neural network is utilized; Finally, residual layer and classification layer are utilized for the integration of convolution results and the classification of hyperspectral image. open-sourcing hyperspectral dataset Piavia, Salinas and Inida are verified, and the algorithm in this paper can improve the hyperspectral classification accuracy efficiently.

Citation Download Citation

Chang Liu, Han Jiang, Yuhui Shi, Panpan Xun, and Renhao Liu "Classification of hyperspectral image based on multi-scale convolutional neural network and attention mechanism", Proc. SPIE 11911, 2nd International Conference on Computer Vision, Image, and Deep Learning, 1191117 (5 October 2021); https://doi.org/10.1117/12.2604557

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