WELN: Siamese Network-based Framework for Geo-localization in Extreme Weather

Cross-view geo-localization is a task of matching the same geographic image from differerent views, e.g., drone and satellite. Due to its GPS-free advantage, cross-view geo-localization is gaining increasing research interest, especially in drone-based localization and navigation applications. In order to guarantee system robustness, existing methods mainly focused on image augmentation and denoising, while facing performace degradation when extreme weather considered. In this paper, we propose an end-to-end image retrieval framework, WELN. By integrating the advanced EVA02 netwotk and LPN algorithm, WELN can extract valuable classification features more efficiently even under extreme weather conditions. Additionally, to enhance model robustness, we expand the University-1652 dataset with nine different weather conditions added. Our method achieves state-of-the-art Recall@1 accuracy on University-1652 dataset, with 92.87% for drone-view target localization task and 93.46% for drone navigation task. Besides, we gain the fourth place in the ACMMM24 Multimedia Drone-Satellite Matching Challenge. Our code will be open sourced at https://github.com/koorter/WELN.