GCG-Net: Graph Classification Geolocation Network

Large-scale visual geolocation is a meaningful task that involves locating a query image by comparing it with images in a database and predicting the most similar image. However, the widely used training framework based on contrastive learning cannot fully utilize all data and is difficult to adapt to larger scales. At the same time, the traditional convolutional neural networks (CNNs) and vector of locally aggregated descriptors (VLADs) using aggregated features cannot fully reflect the relationship between the local features of the image. Therefore, a graph neural network (GNN) is designed as the feature extraction network, and then a training framework based on image classification is constructed. Specifically, a data grouping strategy and special loss function are designed for better training results. After training, we adopt an image retrieval strategy based on kNN for position. In addition, considering that existing datasets cannot be adapted to our requirements, two datasets are constructed for experiments, that one contains large-scale satellite images and the other fuses satellite and unmanned aerial vehicle (UAV) images. Results demonstrate that our method outperforms other common methods in both the datasets. The results demonstrate the effectiveness of our approach for UAV visual geolocation and provide ideas for future research in this field.