A Cross-View Geolocalization Method Based on Frequency-Spatial Feature Enhancement and Dynamic Margin Constraint
In global navigation satellite system (GNSS)-denied scenarios, cross-view geolocalization (CVGL) provides an effective solution for autonomous uncrewed aerial vehicle (UAV) localization. However, viewpoint and scale differences across platforms may cause variations in texture structures and spatial distributions for the same geographic region, making it more difficult to learn consistent and discriminative cross-view representations in CVGL. Meanwhile, visually similar but geographically distinct samples can reduce the separability between true matches and hard negatives. To address these issues, we propose the frequency-spatial feature enhancement and dynamic margin constraint (FSDC) network, which integrates the frequency-aware recalibrated spatial (FARS) module and the margin-based dynamic contrastive learning (MDCL) strategy. The FARS module enhances local structural representations through bidirectional complementary interaction between frequency-domain and spatial structural features, guiding the shared encoder to learn more consistent cross-view representations, while the MDCL strategy imposes adaptive bounded constraints on hard-negative samples to improve feature discriminability and training stability. Experimental results on three benchmarks show that FSDC provides a favorable tradeoff among cross-view retrieval accuracy, model complexity, and inference efficiency.