Enhancing Cross-View Geo-Localization with Domain Alignment and Scene Consistency

Cross-View Geo-Localization task is aimed at establishing correspondences between images captured from different perspectives within the same geographical region. The major challenge lies in the significant appearance variations of the same scene in different views. Current methods predominantly rel...

Full description

Saved in:
Bibliographic Details
Published inIEEE transactions on circuits and systems for video technology p. 1
Main Authors Xia, Panwang, Wan, Yi, Zheng, Zhi, Zhang, Yongjun, Deng, Jiwei
Format Journal Article
LanguageEnglish
Published IEEE 13.08.2024
Subjects
Contrastive Representation Learning
Cross-View
Drones
Feature extraction
Geo-Localization
Image Retrieval
Location awareness
Representation learning
Satellites
Task analysis
Training
Online AccessGet full text

Cover

More Information
Summary:Cross-View Geo-Localization task is aimed at establishing correspondences between images captured from different perspectives within the same geographical region. The major challenge lies in the significant appearance variations of the same scene in different views. Current methods predominantly rely on learning a representation of the coarse-grained information from images and then evaluating the similarity, while the fine-grained features are usually not well-treated. In this paper, a novel method, named DAC (Domain Alignment and scene Consistency) is proposed, which leverages contrastive learning to acquire the global information of images and simultaneously employs a domain space alignment module to align the fine-grained features. The comprehensive utilization of multi-grained vision information guarantees better feature representations. Additionally, a cross-batch scene consistency strategy is proposed in the network to establish the global supervision of the positive samples based on scene correspondence, which improves the distinctiveness of the image representations. Advanced performance is shown by our method in drone-view target localization and drone navigation applications, outperforming state-of-the-art methods in comprehensive tests on the popular public datasets University-1652 and SUES-200. Our method also outperforms existing methods in cross-region localization, showing an average improvement of 5.6% in the R@1. Our codes and models are available at https://github.com/SummerpanKing/DAC.
ISSN:1051-8215
DOI:10.1109/TCSVT.2024.3443510