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 $\beta$ on the matching accuracy, and the best matching results are obtained when $\beta =0.8$.

Suitable-area selection reduces candidate-search costs and mismatch risk in road vector map matching. Existing image-based assessments primarily characterize grayscale, texture, and edge saliency, whereas the suitability of road vector maps depends on road continuity, intersection organization, and topological uniqueness. This study proposes a high-confidence suitable-area selection framework based on a Hypergraph-Enhanced Graph Attention Network (HG-GAT). A road-network suitability dataset is constructed using offline matching results. Candidate regions are represented as road graphs, with semantic hyperedges encoding road chains, intersection stars, and key-node neighborhoods. Hypergraph propagation and graph attention jointly learn high-order structural relations and pairwise road connections, while dual-channel readout integrates regional organization and salient local structures. On the Wuhan test data, HG-GAT achieves an accuracy of 0.8074, an F1-score of 0.8309, and a Precision@10% of 0.8980, the highest values for these metrics among the compared methods. It retains high-confidence candidates with a recall of 0.9467 while reducing false selections relative to the image-based and ordinary graph-learning baselines. The results support the value of semantic high-order topology for distinguishing structurally effective road regions and prioritizing candidates for subsequent matching.