Swin transformer for feature extraction: a cross-view geo-localization method for UAV-Satellite views
Jinyu Liu, Kan Ren, Qian Chen · International Journal of Remote Sensing · 2025
Compared to traditional unmanned aerial vehicle (UAV) geo-localization methods relying on the Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS), the absolute visual localization method, which achieves cross-view image matching between UAV and satellite views, has tremendous potential. Not only can it achieve high accuracy, but it can also replace GNSS when satellite signals are interfered with or interrupted, which is of great practical significance. Based on the Swin Transformer, the cross-view geo-localization method constructs a dual-branch deep neural network architecture with UAV and satellite branches. The main innovation of this method lies in the introduction of the Swin Transformer, which is applied to the feature extraction task of the dual-branch network. By calculating the cosine distance between different view features, the most similar feature vector set is searched, and the images are sorted from large to small to achieve geo-localization functionality. Another contribution of this paper is the use of infrared campus images collected by UAVs as a supplementary test set to verify the model’s generalization ability to different modal images, and preliminary research on UAV night-time positioning tasks.