CCIGeo: Cross-View and Cross-Day-Night Image Geo-Localization Using Daytime Image Supervision

Nan Wu, Chunfang Yang, Baojun Qi, Ma Zhu, Jiangshan Li, Xiangyang Luo

Abstract: Cross-view image geo-localization is a technique to determine the geographic location of the query image by matching it with geo-tagged aerial images. However, when the query image is captured at nighttime, the existing methods could not extract geographic-related information from low and uneven illumination areas effectively, thus geo-localizing the nighttime ground image with poor performance. In this work, we propose a cross-view and cross-day-night image geo-localization method (CCIGeo), which contains three branches, taking the query nighttime ground image, the supervision daytime ground image, and the reference satellite image as inputs, respectively. Inspired by knowledge distillation, the proposed method takes daytime ground image branch as the teacher model, which would supervise the nighttime ground image branch to overcome the interference of the uneven and low illumination, and pay more attention to the areas containing rich geographic-related information. And to better adapt to the cross-day-night environment, a dual-constraint loss function is designed inspired by the concept of knowledge distillation. Extensive experimental results show that CCIGeo significantly improves the performance on nighttime image geo-localization, exceeding the state-of-the-art (SOTA) methods by 1.83%, 3.84%, and 1.64% on three datasets.

External IDs:dblp:journals/tmm/WuYQZLL25