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Spectral-Spatial Attention Networks for Hyperspectral Image Classification

Electronic Information School, Wuhan University, Wuhan 430072, China
Institute of Aerospace Science and Technology, Wuhan University, Wuhan 430072, China
Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(8), 963;
Received: 12 March 2019 / Revised: 15 April 2019 / Accepted: 20 April 2019 / Published: 23 April 2019
(This article belongs to the Special Issue Robust Multispectral/Hyperspectral Image Analysis and Classification)
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Many 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
Keywords: hyperspectal image classification; attention mechanism; RNN; CNN hyperspectal image classification; attention mechanism; RNN; CNN

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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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.

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