Direction-Guided Multiscale Feature Fusion Network for Geo-Localization

Cross-view geo-localization has been widely used as an important technique for determining the geographical location of unmanned aerial vehicles (UAV). Despite various image retrieval methods proposed, drone and satellite image cross-view geo-localization still remains challenging due to their wildly inconsistent view angles. In this paper, we propose a new framework, Swin-Radial-Locality Network (SRLN), to extract robust image feature representations. Specifically, SRLN is based on a pruned version of the Swin Transformer, which integrates multi-scale feature aggregation within a Siamese network structure, featuring shared weights and equipped with multi-classification heads. SRLN is mainly comprised of a Radial-Slicer-Network (RSN) and a Local-Pattern-Network (LPN), which is designed to effectively harmonize directional information from drone-captured images and broader environmental features from satellite imagery, crucial for capturing angle and feature details between drone and satellite images. The RSN part focuses on capturing fine-grained features that represent the drone's directional information, while the LPN is utilized for a more comprehensive analysis of broader environmental features. Extensive experiments are carried out on widely used public benchmark datasets, i.e., University-1652 and SUES-200. With more than 3% improvement over existing methods in both drone-view target localization tasks and drone navigation applications, the results validate the superior performance of our multi-scale feature fusion model, achieving a state-of-the-art performance record.