AttFeat: Attention-Based Features for Infrared and Visible Remote Sensing Image Matching

Infrared and visible remote sensing image matching is significant for the utilization of remote sensing images to obtain scene information. However, due to the large number of sparse and repetitive texture regions in multi-modal remote sensing scenes, feature extraction poses serious difficulties. To address these challenges, this letter proposes Attention-based Features (AttFeat). First, to solve the problem of coarse feature representation, we propose the Parallel Channel and Spatial Attention (PCSA) Module, which focuses on important spatial locations and provides richer cross-channel representation. Second, to address the lack of contextual information, we propose the Squeezed-Axis and Window Transformer (SAWFormer), which can obtain a dense global receptive field at a lower cost while retaining rich details. Finally, multi-modal recoupling loss is utilized to optimize the relationship between the rich feature description and large receptive field. Extensive experiments on aviation and remote sensing multi-modal datasets demonstrate the superiority of our algorithm and the effectiveness of the proposed modules. In terms of detection and matching performance, AttFeat outperforms the baseline ReDFeat by 10.81% and 15.48%, respectively. The dataset and code will be released at: AttFeat.