Sat-RoMa: Cross-Scale Dense Matching for Multi-Temporal UAV-To-Orthophoto Registration

Abstract

Reliable Global Navigation Satellite System (GNSS) signals are increasingly denied or jammed in real-world applications, such as search and rescue operations. In such scenarios, Unmanned Aerial Vehicles (UAVs) must rely on downward-facing cameras for absolute localization against reference satellite maps. While Visual Inertial Odometry (VIO) is highly accurate locally, it inevitably accumulates drift over time. Localizing a drone image against a pre-existing satellite map (e.g., Google Earth) via homography estimation is a viable solution, but it is severely challenged by seasonal variations, construction, and vegetation changes. In this paper, we propose Sat-RoMa, an end-to-end robust dense feature matcher adapted from the state-of-the-art RoMa architecture. By utilizing a frozen, pre-trained DinoV3 encoder specifically tuned for satellite imagery, and formulating the task as matching a small drone image to a 4x larger reference map, Sat-RoMa explicitly handles scale discrepancies and temporal appearance changes. Preliminary results demonstrate that Sat-RoMa significantly outperforms baselines like LoFTR and LightGlue, achieving a 16.0% scale error compared to over 100% for existing methods, paving the way for robust GPS-denied UAV navigation.