Tower-based surveillance (TBS) systems provide continuous, high-resolution, and real-time monitoring over wide spatial extents. However, accurate geolocation of TBS imagery remains largely underexplored due to large viewpoint discrepancies, nonlinear radiometric distortions, and limited spatial overlap with satellite imagery. To address these challenges, we propose TowerSatLoc, a high-precision and fully automated correspondence-based geolocation framework that registers TBS imagery to geo-referenced satellite images under complex conditions. The framework first applies a Bird’s Eye View transformation using intrinsic and extrinsic camera parameters to reduce viewpoint discrepancies. Within this process, an adaptive projection factor and an automatic orientation estimation method are introduced to improve geometric consistency. To handle severe cross-domain differences, semantic representations extracted from DINOv2 are fused with multi-scale geometric features from VGG19 and refined through hierarchical residual matching and confidence-aware resampling. The framework further incorporates robustness-enhancing mechanisms to improve localization reliability under extreme viewpoint variations and uncertain tower geocoordinates. Extensive experiments on a newly constructed dataset, TowSatData, demonstrate the effectiveness and robustness of TowerSatLoc across diverse real-world scenarios, providing a scalable and generalizable solution for correspondence-driven cross-view geolocation.
Chen et al. (Thu,) studied this question.