Robust Registration of Multimodal Remote Sensing Images Using Steerable Hermite Filters and Geometric Constraints

Abstract

Co-registration of multimodal remote sensing images (MRSIs) remains a challenging task due to nonlinear radiometric differences (NRD) and geometric distortions. This letter proposes a robust registration method based on steerable Hermite filters and geometric constraints. The method consists of three stages: coarse registration, image matching, and fine registration. In the first stage, geo-referencing information is used to partition the overlapping region into subblocks for initial alignment. In the second stage, steerable Hermite filters are applied to extract multiscale and multiorientation structural features, which are fused into a principal moment map to detect highly repeatable feature points. A structural descriptor is subsequently constructed from an orientation index map (OIM) generated through hierarchical pooling, enabling robust matching under NRD. In the final stage, an iterative optimization strategy incorporating geometric constraints is employed to eliminate mismatches and refine the registration. Experimental results on multiple MRSI datasets demonstrate that the proposed method outperforms state-of-the-art approaches in terms of both matching robustness and registration accuracy.