A Spatial-Spectrum Fully Attention Network for Band Selection of Hyperspectral Images

Hongqi Zhang, Hongmin Gao, He Sun, Xu Sun, Bing Zhang

Abstract: Deep-learning (DL)-based unsupervised band selection (UBS) methods have received more attention, but the majority of current approaches face challenges associated with striking a balance between computational burden and the UBS performance, and the spatial-spectral information has not been fully investigated. With the aim of addressing these issues, we have proposed a novel method called spatial-spectrum fully attention network (SSFAN), which includes a spatial-spectral samples generator (SSSG) and a nearest neighbor scoring (NNS) module. Aiming to improve the UBS performance without a huge computational burden, the SSSG can directly generate numerous nonoverlapped samples for the input of DL model, where the global spatial-spectral information is used in a more efficient way. For the purpose of further improving the robustness of SSFAN, the NNS can assign different weights to each band by jointly exploiting the prior knowledge in both spatial and spectral domains. Note that the NNS considered the time consumption when investigating the spatial-spectral prior information, so this does not conflict with the problem of UBS balance. We have conducted experiments on three commonly used remote sensing hyperspectral image (HSI) datasets, where our proposed methods have shown a more effective and robust performance than current state-of-the-art approaches. The source code will be made publicly available at https://github.com/duang33/SSFAN .