Dual-branch Pattern and Multi-scale Context Facilitate Cross-view Geo-localization

Cross-view geo-localization aims to locate the target image of the same geographic location from different viewpoints, which is a challenging task in the field of computer vision. Due to the interference of similar images and the surrounding environment of the target building, the matching accuracy is significantly reduced when facing complex scenes. To solve this problem, we propose a cross-view geo-localization method based on dual-branch pattern and multi-scale context to provide a solution for challenging dataset with numerous distractors. This method exploits a Transformer feature extraction network to reduce the loss of fine-grained features. Meanwhile, a dual-branch structure is designed to capture image semantic information and local context information bidirectionally, which can effectively deal with the problem of more interference items in satellite images and improve the accuracy of geographic location tasks in complex scenes. After quantitative experimental verification, both recall rate (Recall) and image retrieval average precision (AP) indicators have been significantly improved on benchmark dataset University-1652 and challenging dataset University-160K, our method can achieve advanced cross-view geo-localization performance.