Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Remote Sensing

DOI: 10.3390/rs17173045

Authors: Jiaqi Li, Yuli Sun, Yaobing Xiang, Lin Lei

Institution: National University of Defense Technology

Language: English

Topic: Advanced Image and Video Retrieval Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,258

Funders: National Natural Science Foundation of China, China Postdoctoral Science Foundation, Natural Science Foundation of Hainan Province