A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 19; p. 4667
• Main Authors: Cui, Zhuofan, Zhou, Pengwei, Wang, Xiaolong, Zhang, Zilun, Li, Yingxuan, Li, Hongbo, Zhang, Yu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-10-2023
• Subjects: Accuracy; Algorithms; cross-view; Data augmentation; Drone aircraft; Drones; Feature extraction; geo-localization; Global positioning systems; GPS; Image retrieval; Information processing; Localization; Localization method; Methods; satellite; Satellite imagery; Satellite navigation systems; Satellites; Training; transformer; UAV; Unmanned aerial vehicles
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15194667

Geo-localization has been widely applied as an important technique to get the longitude and latitude for unmanned aerial vehicle (UAV) navigation in outdoor flight. Due to the possible interference and blocking of GPS signals, the method based on image retrieval, which is less likely to be interfered with, has received extensive attention in recent years. The geo-localization of UAVs and satellites can be achieved by querying pre-obtained satellite images with GPS-tagged and drone images from different perspectives. In this paper, an image transformation technique is used to extract cross-view geo-localization information from UAVs and satellites. A single-stage training method in UAV and satellite geo-localization is first proposed, which simultaneously realizes cross-view feature extraction and image retrieval, and achieves higher accuracy than existing multi-stage training techniques. A novel piecewise soft-margin triplet loss function is designed to avoid model parameters being trapped in suboptimal sets caused by the lack of constraint on positive and negative samples. The results illustrate that the proposed loss function enhances image retrieval accuracy and realizes a better convergence. Moreover, a data augmentation method for satellite images is proposed to overcome the disproportionate numbers of image samples. On the benchmark University-1652, the proposed method achieves the state-of-the-art result with a 6.67% improvement in recall rate (R@1) and 6.13% in average precision (AP). All codes will be publicized to promote reproducibility.