One-to-Many Retrieval Between UAV Images and Satellite Images for UAV Self-Localization in Real-World Scenarios

• Published in: Remote sensing (Basel, Switzerland) Vol. 17; no. 17; p. 3045
• Main Authors: Li, Jiaqi, Sun, Yuli, Xiang, Yaobing, Lei, Lin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2025
• Subjects: Accuracy; Datasets; Drones; Embedding; Flying-machines; geo-localization; Image processing; Image retrieval; Localization; Matching; Methods; Satellite imagery; Tiles; transformer; unmanned aerial vehicle; Unmanned aerial vehicles
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs17173045

Matching drone images to satellite reference images is a critical step for achieving UAV self-localization. Existing drone visual localization datasets mainly focus on target localization, where each drone image is paired with a corresponding satellite image slice, typically with identical coverage. However, this one-to-one approach does not reflect real-world UAV self-localization needs as it cannot guarantee exact matches between drone images and satellite tiles nor reliably identify the correct satellite slice. To bridge this gap, we propose a one-to-many matching method between drone images and satellite reference tiles. First, we enhance the UAV-VisLoc dataset, making it the first in the field tailored for one-to-many imperfect matching in UAV self-localization. Second, we introduce a novel loss function, Incomp-NPair Loss, which better reflects real-world imperfect matching scenarios than traditional methods. Finally, to address challenges such as limited dataset size, training instability, and large-scale differences between drone images and satellite tiles, we adopt a Vision Transformer (ViT) baseline and integrate CNN-extracted features into its patch embedding layer.