GeoCFR: Multimodal Remote Sensing Image Registration via Structural Representation Learning and Posterior Aggregation

High-precision registration of multimodal remote sensing images (MRSIs) is fundamental to multi-source information fusion and Earth observation applications. However, cross-modal appearance differences combined with large rotations, translations, and scale changes make reliable correspondences difficult to establish. Sparse methods can handle large initial displacements, but cross-modal differences can result in too few reliable correspondences or uneven spatial coverage; dense methods provide more candidates but do not fully exploit candidate distributions in local responses for accurate global geometry estimation. We therefore propose GeoCFR, which combines cross-modal structural representation learning with local matching posterior aggregation to progressively refine a global affine transformation. The regional moment–relational spectrum descriptor (RMSD) uses regional statistics and multi-ring relationships to obtain an affine initialization; the Posterior-Aggregating Tokens for Affine Refinement (PATAR) module aggregates local candidate distributions and geometric information to predict global affine residuals at two feature scales. We also construct WH-M3R, a dataset covering Wuhan with 343,758 valid cross-modal image pairs across 13 data layers at spatial resolutions of 0.5, 2, 5, and 10 m. Match-30K is used for training and validation, and Match-1K for testing. Under the High protocol on WH-M3R-Match-1K, GeoCFR improves the correct matching rate (CMR) at the 1-, 2-, and 3-pixel thresholds by 7.7, 6.9, and 5.8 percentage points over the strongest baseline at each threshold. Under the High protocol, GeoCFR achieves leading overall zero-shot performance on SOMA-1M-Test, RoadScene, and OSdataset, demonstrating transfer across datasets under large geometric changes.