Cosine Similarity Template Matching Networks for Optical and SAR Image Registration

Abstract

Synthetic aperture radar (SAR) and optical imagery are complementary methods in Earth observation. However, traditional similarity measures struggle to accurately align these heterogeneous images due to sensor differences and modality disparities. We propose a cosine similarity template matching network to address this challenge. Our approach leverages spatial search operations and cosine similarity to effectively quantify similarities between SAR and optical images. We introduce a pooling heatmap loss with label transform operation to facilitate smoother convergence. This method precisely identifies matching regions in heterogeneous datasets, significantly outperforming state-of-the-art methods. Moreover, we construct comprehensive datasets comprising spring, summer, fall, and winter subsets derived from SEN1-2 datasets, each containing diverse SAR and optical image pairs. These datasets serve as benchmarks for evaluating template matching algorithms in heterogeneous image scenarios, setting the stage for further advancements in template matching research.

Keywords

• Deep learning
• optical image
• synthetic aperture radar (SAR)
• template matching