Currently

unmanned aerial vehicles (UAVs) primarily rely on the global navigation satellite system (GNSS) for navigation and localization. However

in scenarios where satellite signals are weak or interfered with

the completion of UAV missions is severely affected

and the safe flight of UAVs could even be jeopardized. To address this issue

this paper proposes a visual localization algorithm to ensure the safe and long-term flight of UAVs in GNSS-denied environments. The algorithm calculates the UAV's geographic coordinates by matching aerial images captured by the UAV with geotagged satellite maps. Firstly

a satellite map preprocessing method is designed to reduce the computational load during UAV flights. Secondly

the learned perceptual image patch similarity (LPIPS) metric is used for initial retrieval. Finally

image matching and offset estimation are performed by combining the deep learning-based SuperPoint sparse feature extraction algorithm and the LightGlue feature matching algorithm

thus finally achieving UAV visual localization. The proposed method is tested on the ALTO dataset

achieving a 17.2% improvement over the current state-of-the-art methods in terms of the R@1 metric

which demonstrates its feasibility and advancement.