Attention-based neural network with Generalized Mean Pooling for cross-view geo-localization between UAV and satellite

Cross-view geo-localization is finding images containing the same geographic target in multi-views. For example, given a query image from UAV view, a proposed matching model can find an exact image of the same location in a gallery collected by satellites. Using a UAV-view image to acquire the true-matched satellite-view image with a geo-tag, the current geographic location of the UAV can be easily localized based on flight records. However, due to the extreme change of viewpoints across platforms, traditional image processing methods have met difficulties matching multi-view images. This paper proposed advanced neural network-based approaches, which applied the attention mechanism to the feature learning process to improve the ability to learn essential features from the input image. A different pooling method was also implemented to increase the global descriptor. Our proposed models have significantly improved accuracy and have achieved competitive results on the University-1652 dataset.