A Multi-Scale SAR-Optical Image Matching Method Using Structure-Enhanced Convolutional Layer and Transformer-CNN Model

Due to the geometric and nonlinear radiometric differences (NRD) between SAR and optical images, coupled with the impact of speckle noise, achieving robust and high-precision matching between them remains challenging. This paper introduces a novel multi-scale SAR-optical image matching method, which employs an improved multi-scale matching strategy. Specifically, we first establish an image pyramid and detect geometric keypoints of the images, subsequently segmenting the images into sub-blocks centered around these keypoints. These sub-blocks are then fed into the proposed siamese feature description network, comprising of a structure-enhanced convolutional layer and a Transformer-CNN model, for feature descriptors extraction, followed by feature matching. Finally, the matching point pairs are obtained by combining the matching relationships between image pairs of different scales. Experimental results show that our proposed algorithm achieves better matching performance compared to the existing state-of-the-art algorithms.