Dual-Task Supervised Network for SAR and Road Vector Image Matching.

Highlights: What are the main findings? We propose a Siamese U-Net dual-task supervised network (SUDS) that simultaneously learns SAR-road-vector matching and road segmentation, achieving 80.2% localization accuracy within a 5-pixel registration error and 91.0% within a 10-pixel registration error. We built a large-scale SAR-VEC dataset (2503 precisely aligned SAR/vector pairs) and show that the joint matching–segmentation loss suppresses interference more effectively than single-task networks (Unet++, Siam-U, NCC, MatchNet). What are the implications of the main findings? Enables accurate, real-time positioning of SAR imagery against road networks in GNSS-challenged environments, offering a ready-to-use model for navigation applications. Demonstrates that dual-task supervision is an effective tuning-light strategy for cross-modal matching with sparse vector data, providing a reproducible path to boost robustness without extra post-processing. We propose using Synthetic Aperture Radar (SAR) images as real-time images and road vector images as reference images for matching navigation, and propose a Siamese U-Net dual-task supervised network for solving the problem, called SUDS. Unlike existing methods of heterogenous image matching, which extract common features and eliminate saliency differences for matching, we exploit the advantages of the vector images themselves to reduce the matching difficulty from the reference image selection. Firstly, we extract the common road features between SAR images and road vector images using a weight-sharing U-Net feature extraction network. Then, we propose to weight the sum of segmentation loss and matching loss as the network loss to optimize the feature extraction efficiency from both segmentation and matching perspectives. We prepare a specialized SAR-VEC dataset for experiments. Experiments show that the method is able to obtain high matching correctness, with 80.2% correctness within 5 pixels of matching error and 91.0% correctness within 10 pixels of matching error. Compared to existing methods, this method is able to identify the differences in similar roads and better eliminate the influence of imaging interference in SAR images on the matching results, obtaining more accurate matching results with better robustness. And we explore the effect of different weighting parameters β on the matching accuracy, and the best matching results are obtained when β = 0.8 . [ABSTRACT FROM AUTHOR]

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