Joint Representation Learning Based on Feature Center Region Diffusion and Edge Radiation for Cross-View Geo-Localization

The essence of the cross-view geo-localization task is to accurately identify the same object across images captured from different viewpoints. Due to variations in image acquisition methods and viewing angles, the content information of the images can differ significantly, which may result in localization failure. Therefore, cross-view geo-localization remains a challenging task. To solve this issue, a joint representation learning network based on feature center region diffusion and edge radiation is proposed in this article. First, to extract the crucial information from the global features, we design the central diffusion module that identifies important regions within the features and enhances feature robustness. Additionally, we design an edge radiation mechanism that expands the receptive field and further highlights crucial information in the image to support the central diffusion module in achieving more stable performance. On this basis, we propose an adaptive triple InfoNCE loss function to assist network training, improving the discriminability of the extracted features. Finally, the proposed network is tested on two mainstream datasets, and experimental results demonstrate that the proposed model outperforms the state-of-the-art methods, which can prove its effectiveness.