SOMA: Feature Gradient Enhanced Affine-Flow Matching for SAR-Optical Registration

Achieving pixel-level registration between SAR and optical images remains a challenging task due to their fundamentally different imaging mechanisms and visual characteristics. Although deep learning has achieved great success in many cross-modal tasks, its performance on SAR-Optical registration tasks is still unsatisfactory. Gradient-based information has traditionally played a crucial role in handcrafted descriptors by highlighting structural differences. However, such gradient cues have not been effectively leveraged in deep learning frameworks for SAR-Optical image matching. To address this gap, we propose SOMA, a dense registration framework that integrates structural gradient priors into deep features and refines alignment through a hybrid matching strategy. Specifically, we introduce the Feature Gradient Enhancer (FGE), which embeds multi-scale, multi-directional gradient filters into the feature space using attention and reconstruction mechanisms to boost feature distinctiveness. Furthermore, we propose the Global-Local Affine-Flow Matcher (GLAM), which combines affine transformation and flow-based refinement within a coarse-to-fine architecture to ensure both structural consistency and local accuracy. Experimental results demonstrate that SOMA significantly improves registration precision, increasing the CMR@1px by 12.29% on the SEN1-2 dataset and 18.50% on the GFGE_SO dataset. In addition, SOMA exhibits strong robustness and generalizes well across diverse scenes and resolutions.

Authors

• Haodong Wang School of Computer Science, Northwestern Polytechnical University Shaanxi Provincial Key Lab. of Speech and Image Information Processing National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology
• Tao Zhuo Shandong Artificial Intelligence Institute, Qilu University of Technology (Shandong Academy of Sciences)
• Xiuwei Zhang School of Computer Science, Northwestern Polytechnical University Shaanxi Provincial Key Lab. of Speech and Image Information Processing National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology
• Hanlin Yin School of Computer Science, Northwestern Polytechnical University Shaanxi Provincial Key Lab. of Speech and Image Information Processing National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology
• Wencong Wu School of Computer Science, Northwestern Polytechnical University Shaanxi Provincial Key Lab. of Speech and Image Information Processing National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology
• Yanning Zhang Northwestern Polytechnical University

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