Grid-Reg: Detector-Free Gridized Feature Learning and Matching for Large-Scale SAR-Optical Image Registration

• URL: http://arxiv.org/abs/2507.04233v2
• Date: Wed, 03 Sep 2025 04:09:53 GMT
• Title: Grid-Reg: Detector-Free Gridized Feature Learning and Matching for Large-Scale SAR-Optical Image Registration
• Authors: Xiaochen Wei, Weiwei Guo, Zenghui Zhang, Wenxian Yu,
• Abstract summary: It is highly challenging to register large-scale, heterogeneous SAR and optical images, particularly across platforms.<n>To overcome these challenges, we propose Grid-Reg, a grid-based multimodal registration framework.<n>Our proposed approach achieves superior performance over state-of-the-art methods.
• Score: 22.80821597640134
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It is highly challenging to register large-scale, heterogeneous SAR and optical images, particularly across platforms, due to significant geometric, radiometric, and temporal differences, which most existing methods struggle to address. To overcome these challenges, we propose Grid-Reg, a grid-based multimodal registration framework comprising a domain-robust descriptor extraction network, Hybrid Siamese Correlation Metric Learning Network (HSCMLNet), and a grid-based solver (Grid-Solver) for transformation parameter estimation. In heterogeneous imagery with large modality gaps and geometric differences, obtaining accurate correspondences is inherently difficult. To robustly measure similarity between gridded patches, HSCMLNet integrates a hybrid Siamese module with a correlation metric learning module (CMLModule) based on equiangular unit basis vectors (EUBVs), together with a manifold consistency loss to promote modality-invariant, discriminative feature learning. The Grid-Solver estimates transformation parameters by minimizing a global grid matching loss through a progressive dual-loop search strategy to reliably find patch correspondences across entire images. Furthermore, we curate a challenging benchmark dataset for SAR-to-optical registration using real-world UAV MiniSAR data and Google Earth optical imagery. Extensive experiments demonstrate that our proposed approach achieves superior performance over state-of-the-art methods.

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