Multi-Scale Geo-Localization Based on Local Similarity Area Distance Measurement Method

Cross-view geo-localization is to match remote sensing images from different platforms. The UAV-view image is matched with the satellite-view image with geographical localization information, so as to determine the specific geographical localization of the UAV. Cross-view geo-localization can be applied in many fields. For example, cross-view geo-localization can assist traditional satellite positioning to improve accuracy or perform positioning tasks independently. The main challenge now is that there are great differences in remote sensing images obtained by satellites and UAVs, such as changes in viewpoints and differences in scales. The current method mainly studies the influence of viewpoint change, while ignoring the scale difference between cross-source images caused by different resolutions. In order to reduce the influence of scale differences, We propose a network structure called local similarity network (LSNet) based on the siamese network and multi-scale sliding windows. LSNet adopts a new distance measurement method based on the most similar area. Experiments show that our method has excellent performance in multi-scale remote sensing geo-localization.