A Novel Rotation and Scale Equivariant Network for Optical-SAR Image Matching

There are large nonlinear radiance differences between optical and synthetic aperture radar (SAR) from different imaging sensors. When combined with rotational and scale variations, achieving equivariant matching becomes highly challenging. The existing single network models face two primary limitations: insufficient learning of modal invariant features and insufficient characterization of rotation and scale. After task decomposition, we propose a novel rotation and scale equivariant network (RSENet) for optical-SAR image matching, comprising two modules: SAR-to-optical image translation module and equivariant descriptors (EDs). First, we propose an image translation network to obtain more beneficial pseudo-optical images for matching. Second, we achieve high-precision matching via EDs designed through scale angle estimation (SAE) and descriptor colearning. The network initially corrects image blocks explicitly via scale and angle loss functions that are based on periodic encoding (PE) learning. Subsequently, it employs an attention module based on cross-scale perception and feature channel excitation to extract hierarchical information from the corrected image blocks and enhance the local feature representation of the descriptors. We validate the effectiveness of RSENet by conducting numerous experiments under combined transformations of various angles and scales. Our proposed method achieves an approximately 28% improvement in the matching success rate compared with existing methods.