Spectral-Spatial Attention Networks for Hyperspectral Image Classification
AbstractMany deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN), have been successfully applied to extracting deep features for hyperspectral tasks. Hyperspectral image classification allows distinguishing the characterization of land covers by utilizing their abundant information. Motivated by the attention mechanism of the human visual system, in this study, we propose a spectral-spatial attention network for hyperspectral image classification. In our method, RNN with attention can learn inner spectral correlations within a continuous spectrum, while CNN with attention is designed to focus on saliency features and spatial relevance between neighboring pixels in the spatial dimension. Experimental results demonstrate that our method can fully utilize the spectral and spatial information to obtain competitive performance. View Full-Text
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Mei, X.; Pan, E.; Ma, Y.; Dai, X.; Huang, J.; Fan, F.; Du, Q.; Zheng, H.; Ma, J. Spectral-Spatial Attention Networks for Hyperspectral Image Classification. Remote Sens. 2019, 11, 963.
Mei X, Pan E, Ma Y, Dai X, Huang J, Fan F, Du Q, Zheng H, Ma J. Spectral-Spatial Attention Networks for Hyperspectral Image Classification. Remote Sensing. 2019; 11(8):963.Chicago/Turabian Style
Mei, Xiaoguang; Pan, Erting; Ma, Yong; Dai, Xiaobing; Huang, Jun; Fan, Fan; Du, Qinglei; Zheng, Hong; Ma, Jiayi. 2019. "Spectral-Spatial Attention Networks for Hyperspectral Image Classification." Remote Sens. 11, no. 8: 963.
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