Grid-Reg: Grid-Based SAR and Optical Image Registration Across Platforms

• URL: http://arxiv.org/abs/2507.04233v1
• Date: Sun, 06 Jul 2025 03:43:18 GMT
• Title: Grid-Reg: Grid-Based SAR and Optical Image Registration Across Platforms
• Authors: Xiaochen Wei, Weiwei Guo, Zenghui Zhang, Wenxian Yu,
• Abstract summary: We propose a novel grid-based multimodal registration framework (Grid-Reg) across airborne and space-born platforms.<n>Our Grid-Reg is based on detector-free matching and global loss rather than accurate keypoint correspondences.<n>We curate a new challenging benchmark dataset of SAR-to-optical registration using real-world UAV MiniSAR data and optical images from Google Earth.
• Score: 15.780384238431743
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Registering airborne SAR with spaceborne optical images is crucial for SAR image interpretation and geo-localization. It is challenging for this cross-platform heterogeneous image registration due to significant geometric and radiation differences, which current methods fail to handle. To tackle these challenges, we propose a novel grid-based multimodal registration framework (Grid-Reg) across airborne and space-born platforms, including a new domain-robust descriptor extraction network, Hybrid Siamese Correlation Metric Learning Network (HSCMLNet) and a grid-based solver (Grid-solver) for transformation parameters estimation. Our Grid-Reg is based on detector-free and global matching loss rather than accurate keypoint correspondences. These accurate correspondences are inherently difficult in heterogeneous images with large geometric deformation. By Grid-Solver, our Grid-Reg estimates transformation parameters by optimizing robust global matching loss-based patch correspondences of whole images in a coarse-to-fine strategy. To robustly calculate the similarity between patches, specifically that have noise and change objects, we propose HSCMLNet, including a hybrid Siamese module to extract high-level features of multimodal images and a correlation learning module (CMLModule) based equiangular unit basis vectors (EUBVs). Moreover, we propose a manifold loss EUBVsLoss to constrain the normalized correlation between local embeddings of patches and EUBVs. Furthermore, we curate a new challenging benchmark dataset of SAR-to-optical registration using real-world UAV MiniSAR data and optical images from Google Earth. We extensively analyze factors affecting registration accuracy and compare our method with state-of-the-art techniques on this dataset, showing superior performance.

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