Multimodal image registration is a fundamental yet challenging task, particularly in remote sensing scenarios involving cross-platform, multi-temporal, and cross-modal data. The primary difficulty arises from the coexistence of large-scale geometric distortions and complex local appearance variations across modalities, which makes it difficult for a single-stage model to achieve both global alignment and fine-grained correspondence simultaneously. To address this issue, we propose MMARNet, a task-driven coarse-to-fine registration framework that explicitly decomposes multimodal registration into global geometric alignment and local correspondence refinement. Instead of treating registration as a unified problem, the proposed framework sequentially resolves distinct sources of error, leading to improved robustness and accuracy under challenging conditions. In the first stage, MMARNet learns geometry-aware global alignment by identifying structurally reliable regions across modalities and estimating large-scale transformations, effectively reducing the initial misalignment and normalizing the geometric space. In the second stage, the model focuses on residual local discrepancies by learning context-enhanced feature representations, enabling robust keypoint-level matching even under severe modality differences and nonlinear distortions. The two stages are designed to work in a complementary manner, where global alignment significantly simplifies the subsequent local matching process. Extensive experiments on three challenging multimodal datasets demonstrate that MMARNet achieves superior performance in both accuracy and robustness compared to existing methods. The results validate the effectiveness of the proposed problem decomposition and highlight the advantage of the coarse-to-fine optimization strategy for multimodal remote sensing image registration.
Liu et al. (Mon,) studied this question.