Cross-modal Thermal Geo-localization (TG) provides a robust, all-weather solution for Unmanned Aerial Vehicles (UAVs) in Global Navigation Satellite System (GNSS)-denied environments. However, profound thermal-visible modality gaps introduce severe feature ambiguity, systematically corrupting conventional coarse-to-fine registration. To dismantle 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 (SGVA) module to adaptively optimize satellite crop regions, effectively correcting initial spatial deviations. Second, we develop the Cascaded Spatial-Adaptive Texture-Structure Filtering (C-SATSF) mechanism to explicitly enforce geometric consistency, thereby eradicating dense cross-modal outliers. Finally, we propose the Consensus-Driven Reliability-Aware Position Selection (CD-RAPS) strategy to derive the optimal solution through a synergy of physically constrained pose optimization. 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 (DSM). 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. Code and dataset are available at https://github.com/FloralHercules/SCC-Loc.

翻译：跨模态热红外地理定位（TG）为全球导航卫星系统（GNSS）拒止环境中的无人机（UAV）提供了鲁棒的全天候解决方案。然而，深层热红外-可见光模态差异引入了严重的特征歧义，系统性地破坏了传统的由粗到精配准。为突破这一瓶颈，我们提出SCC-Loc——一种统一的语义-级联-共识定位框架。通过在全球检索和MINIMA$_{\text{RoMa}}$匹配中共享单一DINOv2骨干网络，该框架最小化内存占用并实现零样本、高精度的绝对位置估计。具体而言，我们通过引入三个协同模块解决模态歧义：首先，设计语义引导视口对齐（SGVA）模块自适应优化卫星裁剪区域，有效纠正初始空间偏差；其次，开发级联空间自适应纹理结构滤波（C-SATSF）机制显式强制几何一致性，从而消除密集跨模态离群点；最后，提出共识驱动可靠性感知位置选择（CD-RAPS）策略，通过物理约束位姿优化的协同作用推导最优解。针对数据稀缺问题，我们构建Thermal-UAV综合数据集，提供11,890个多样性热红外查询，并配准大规模卫星正射影像及对应空间对齐数字地表模型（DSM）。大量实验表明，SCC-Loc确立了新最优性能，将平均定位误差抑制至9.37米，并在严格5米阈值下较最强基线实现7.6倍精度提升。代码与数据集见https://github.com/FloralHercules/SCC-Loc。