Spectral-Spatial Attention Networks for Hyperspectral Image Classification

• Main Authors: Xiaoguang Mei, Erting Pan, Yong Ma, Xiaobing Dai, Jun Huang, Fan Fan, Qinglei Du, Hong Zheng, Jiayi Ma
• Format: Article
• Language: English
• Published: MDPI AG 2019-04-01
• Series: Remote Sensing
• Subjects: hyperspectal image classification; attention mechanism; RNN; CNN
• Online Access: https://www.mdpi.com/2072-4292/11/8/963

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.