UAV-Satellite View Synthesis for Cross-View Geo-Localization

The goal of cross-view image matching based on geo-localization is to determine the location of a given ground-view image (front view) by matching it with a group of satellite-view images (vertical view) with geographic tags. Due to the rapid development of unmanned aerial vehicle (UAV) technology in recent years, it has provided a real viewpoint close to 45 degrees (oblique view) to bridge the visual gap between views. However, existing methods ignore the direct geometric space correspondence of UAV-satellite views, and only use brute force for feature matching, leading to inferior performance. In this context, we propose an end-to-end cross-view matching method that integrates cross-view synthesis module and geo-localization module, which fully considers the spatial correspondence of UAV-satellite views and the surrounding area information. To be specific, the cross-view synthesis module includes two parts: the oblique view of UAV is first converted to the vertical view by perspective projection transformation (PPT), which makes the UAV image closer to the satellite image; then we use conditional generative adversarial nets (CGAN) to synthesize the UAV image with vertical view style, which is close to the real satellite image by learning the converted UAV as the input image and the real satellite image as the label. Geo-localization module refers to existing local pattern network (LPN), which explicitly considers the surrounding environment of the target building. These modules are integrated in a single architecture called PCL, which mutually reinforce each other. Our method is superior to the existing UAV-satellite cross-view methods, which improves by about 5%.

Automated summary

The goal of cross-view image matching based on geo-localization is to determine the location of a ground-view image by matching it with a group of satellite-view images with geographic tags. Existing methods ignore the spatial correspondence between UAV-satellite views and only use brute force for feature matching, resulting in inferior performance. In this study, we propose an end-to-end cross-view matching method that integrates cross-view synthesis and geo-localization modules, improving performance by about 5%.