Cross-view image geo-localization (CVGL) is a key technique for achieving geographic location matching through multi-source images, with broad applications in fields such as robot navigation and autonomous driving. However, existing methods typically assume that images are directionally aligned, making them less effective in real-world scenarios with directional deviations. To address this challenge, this paper proposes a ranking aggregation-based method for enhancing directional robustness. By introducing random directional shifts to ground-level images and employing mean and min aggregation strategies, the proposed method improves the matching accuracy and robustness. Experiments conducted on the CVUSA_360 and CVACT_360 datasets demonstrate that this approach significantly enhances the performance of various models under directionally misaligned conditions, with particularly outstanding results in terms of the R@1 metric. Visual analysis further verifies the advantages of the method in strengthening directional robustness, suppressing noise, and improving matching accuracy, thus providing an efficient solution for geo-localization tasks in complex scenarios.
SHENG Yining
,
ZHAO Lijun
,
ZHANG Zheng
,
TANG Ping
. A ranking aggregation-based method for enhancing directional robustness in cross-view image geo-localization[J]. Journal of University of Chinese Academy of Sciences, 0
: 7
.
DOI: 10.7523/j.ucas.2025.019
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