Enhancing Semantic Information Representation in Multi-View Geo-Localization through Dual-Branch Network with Feature Consistency Enhancement and Multi-Level Feature Mining

Metric learning is fundamental to multi-view geo-localization, as it aims to establish a distance metric that minimizes the feature space distance between similar data points while maximizing the separation between dissimilar ones. However, in Siamese networks employed for metric learning, individual branches may exhibit discrepancies in their interpretation of semantic information from input data, resulting in semantically inconsistent feature representations. To address this issue, a method is designed to enhance significant region consistency within multi-view spaces by integrating feature consistency enhancement (FCE) and multi-level feature mining (MLFM) techniques into a dual-branch network. The FCE method emphasizes critical components of the input data, ensuring feature consistency between the two branches. Additionally, the MLFM mechanism facilitates feature integration across multiple levels, thereby enabling a more comprehensive extraction of semantic information. This approach enhances semantic understanding and promotes feature consistency across branches. The proposed method achieves AP values of 82.38% for drone-to-satellite and 77.36% for satellite-to-drone image matching. Notably, the method maintains computational efficiency without significantly affecting inference time. Additionally, improvements are observed in R@1, R@5 and R@10 metrics. The experimental results show that integrating FCE and MLFM into the dual-branch network improves semantic representation and outperforms existing methods.