Satellite signal interruptions disable unmanned aerial vehicle (UAV) navigation, making visual localization a vital alternative. To improve robustness in oblique flight paths and complex environments, we propose the UAV visual geo-localization network with transformer and hypergraph feature aggregation enhancement (HTGL). First, we enhanced feature extraction capabilities by optimizing the downsampling strategy and attention allocation mechanism in the backbone network. Second, we designed the Hypergraph Feature Aggregation Enhancement (HFAE) module based on hypergraph-based adaptive correlation enhancement (HyperACE) to improve the model’s ability to capture higher-order correlations. Furthermore, we constructed the Complex Scene Rotation dataset (CSR10) and proposed a method for simulating winter scenes, thereby overcoming the limitations of existing research in terms of scenes, flight directions, and seasons. Additionally, two evaluation metrics—pixel distance root mean square error (PD-RMSE) and geographic distance root mean square error (GD-RMSE)—were introduced to enable a comprehensive assessment of algorithm performance. Experimental results show that HTGL achieved RDS scores of 85.95% (+1.4%), 83.64% (+4.1%), and 91.52% (+1.49%) on the UL14, UL14_ROT, and CSR10 datasets, respectively, demonstrating strong robustness in rotated and complex scenes. Actual deployment and flight tests on the Jetson Orin NX platform further validated the model’s excellent engineering practicality.

In complex environments such as cities and canyons, cross-view matching of UAV and satellite imagery serves as a complementary approach to self-localization in the absence of GNSS signals. To address the limitations of existing datasets—namely their limited variety of scenes and low annotation accuracy—we have constructed the Cross-View, Multi-Altitude, Multi-Scene UAV Localization Dataset (CMHUAV). This dataset covers nine adjacent, non-overlapping subregions in Hangzhou and comprises 8625 precisely matched pairs of drone and satellite images, providing high-precision supervision for cross-view feature learning. Furthermore, we designed a Transformer-based global feature enhancement localization model (SLUG) equipped with a lightweight Adaptive Part Weighting (APW) module, which learns part-level scalar importance weights to dynamically recalibrate the contribution of each annular region. By integrating adaptive part-level weighting with a joint supervision strategy, we significantly enhance SLUG’s spatial discrimination capability. We also propose the Height-aware Normalized Distance Matching (HNDM@K) metric, which unifies retrieval ranking quality and geospatial bias within a single framework, enabling joint evaluation of elevation-adaptive localization accuracy and retrieval performance. Experiments demonstrate that, on the CMHUAV dataset, SLUG achieves a recall of 93.53% and a mean precision of 93.9%; under the HNDM@1 metric, the localization scores at all three altitude levels exceed 94.8%. These results demonstrate that the proposed method, evaluated via offline visual retrieval, exhibits strong potential as a vision-based localization module for UAVs under GNSS disruption. Nevertheless, closed-loop flight tests and system-level localization performance still require further validation.