SCC-Loc: A Unified Semantic Cascade Consensus Framework for UAV Thermal Geo-Localization

Cross-modal Thermal Geo-localization provides a robust, all-weather solution for Unmanned Aerial Vehicles (UAVs) in Global Navigation Satellite System-denied environments. However, profound thermal-visible modality gaps introduce severe feature ambiguity, systematically corrupting conventional coarse-to-fine registration. To address this bottleneck, we propose SCC-Loc, a unified Semantic-Cascade-Consensus localization framework. By sharing a single DINOv2 backbone across global retrieval and MINIMA $_{\text{RoMa}}$ matching, it minimizes memory footprint and achieves zero-shot, highly accurate absolute position estimation. Specifically, we tackle modality ambiguity by introducing three cohesive components. First, we design the Semantic-Guided Viewport Alignment module to adaptively optimize satellite crop regions, effectively correcting initial spatial deviations. Second, we develop the Cascaded Spatial-Adaptive Texture-Structure Filtering mechanism to explicitly enforce geometric consistency, thereby suppressing dense cross-modal outliers. Finally, we propose the Consensus-Driven Reliability-Aware Position Selection strategy to determine the optimal position by integrating physically constrained pose optimization, multidimensional reliability evaluation, and geographic consensus. To address data scarcity, we construct Thermal-UAV, a comprehensive dataset providing 11 890 diverse thermal queries referenced against a large-scale satellite ortho-photo and corresponding spatially aligned Digital Surface Model. Extensive experiments demonstrate that SCC-Loc establishes a new state-of-the-art, suppressing the mean localization error to 9.37 m and providing a 7.6-fold accuracy improvement within a strict 5-m threshold over the strongest baseline.