Spatial-spectral collaborative attention network for hyperspectral unmixing

Abstract

In recent years, the transformer architecture has demonstrated exceptional feature extraction capabilities in the field of computer vision (CV). Building on this, our paper aims to fully exploit the potential of the attention in transformers and apply it to the task of hyperspectral unmixing (HU). We propose the Spatial Spectral Collaborative Attention Network (SSCA-Net) model. We obtain spectral information with continuous spatial attributes from HSIs in advance, and input it into SSCA-Net together with HSIs. The improved self-cross attention can collaboratively extract spatial-spectral domain information of HSIs, thereby obtaining more accurate abundance scores. In addition, we conduct ablation experiments to investigate the influence of attention with various configurations on the performance of the unmixing process. The performance of the proposed model is evaluated on three real-world datasets: Samson, Jasper, and Houston, and compared with the performance of FCLSU, GLMM, DAEU, CNNAEU, CyCU, and DHTN algorithms.

Keywords

• Hyperspectral unmixing
• transformer
• self-cross attention
• spatial-spectral domain information