A dense spatial-spectral attention network for hyperspectral image band selection

Hyperspectral images (HSIs) are usually composed of hundreds of bands, which are highly correlated and redundant, leading to dimension disaster and high complexity of classification. In this paper, we propose an end-to-end dense spatial-spectral attention network (DSSAN) for hyperspectral image band selection to reduce the complexity of classification while ensuring the accuracy. In this network, an embeddable spatial-spectral attention module is designed, which can adaptively select the spectral bands from the raw input data. Moreover, this module is a plug-and-play complementary component and embedded in a dense convolutional network (DenseNet) for end-to-end training. The experimental results on two classic hyperspectral data sets demonstrate that the proposed method is superior to several mainstream band selection methods in classification accuracy and the selected band subset has lower redundancy, which can meet the application requirements.

Automated summary

This paper proposes an end-to-end dense spatial-spectral attention network (DSSAN) for hyperspectral image band selection to reduce classification complexity while maintaining accuracy. Experimental results demonstrate the superiority of this method in classification accuracy.