Spectral-wise Attention-based Residual Network for Hyperspectral Image Classification

Hyperspectral images (HSI) have abundant bands and can capture more useful information, having been widely used in military and civil applications. Traditional HSI classification algorithms failed to take full consideration of the relationship between spatial-wise and spectral-wise information. In this paper, we propose the Spectral-wise Attention-based Residual Network (SARN), in which double branches structure is applied for HSI classification. There are two channels in the model. In the first channel, a novel spectral attention block is used to generate the attention map for the spectral-wise information. Then in the second channel, a spatial-wise residual unit is utilized to draw spatial features. Afterward, the spectral attention map and the spatial features are fused for classification. Experiment results on the Pavia University dataset and Indian_pines dataset demonstrate that the proposed method has better performance than the state-of-art method.