An Attention-Based Fusion for Handcrafted and Deep Feature to Improve Optical and SAR Image Matching

Matching optical and synthetic aperture radar (SAR) images has always been a challenging task in remote sensing image processing. Recently, optical-SAR image matching using deep learning has outperformed traditional methods that rely on handcrafted features. However, most current deep learning methods emphasize capturing deep semantics while disregarding stable handcrafted features. This approach may restrict the advancement of optical-SAR image matching technology. Accordingly, this study introduces an attention-based feature fusion method for integrating handcrafted and deep features, which enhances the performance of optical-SAR image matching. First, we extract handcrafted and deep features separately. Then, an attention module is designed to integrate them for computing the regions of interest. Finally, we use the calculated weights to fuse the handcrafted and deep features for image matching. Compared with single-type features, fused features can better capture the common characteristics of optical and SAR images. The experimental results demonstrate a significant improvement in matching accuracy with the use of merged features.