An Efficient Method based on Multi-view Semantic Alignment for Cross-view Geo-localization

Cross-view geo-localization is to retrieve the most relevant images from different views. The biggest challenge is the visual differences between different views and the location shifts in practical applications. Existing methods usually extract fine-grained features of the retrieval target and match them by semantic alignment. The Transformer-based approach can focus on more contextual information than the CNN-based approach and also learn the geometric correspondence between two viewpoint images directly through the location encoding information. However, the existing methods need to fully utilize the information from different viewpoints, and the model needs to understand the context information sufficiently. To address these issues, we propose an efficient method to fully use image information from cross-views and feature fusion, divided into two branches: Aerial-View Local-Feature Cross-Fusion(ALCF) and Multi-View Global-feature Cross-Fusion(MGCF). By observing the characteristics of the aerial and street views, we perform a targeted fusion of global and local features from different viewpoints. In addition, we introduce a multi-view semantic alignment module, which can solve the problem that more noise information is introduced when the aerial view and street view images are semantically aligned. Experiments show that our proposed method achieves excellent performance in both the drone viewpoint target localization and drone navigation tasks on the University-1652 dataset.