  Unmanned Aerial Vehicle (UAV) visual geo-localization aims to match images of
the same geographic target captured from different views, i.e., the UAV view
and the satellite view. It is very challenging due to the large appearance
differences in UAV-satellite image pairs. Previous works map images captured by
UAVs and satellites to a shared feature space and employ a classification
framework to learn location-dependent features while neglecting the overall
distribution shift between the UAV view and the satellite view. In this paper,
we address these limitations by introducing distribution alignment of the two
views to shorten their distance in a common space. Specifically, we propose an
end-to-end network, called PVDA (Progressive View Distribution Alignment).
During training, feature encoder, location classifier, and view discriminator
are jointly optimized by a novel progressive adversarial learning strategy.
Competition between feature encoder and view discriminator prompts both of them
to be stronger. It turns out that the adversarial learning is progressively
emphasized until UAV-view images are indistinguishable from satellite-view
images. As a result, the proposed PVDA becomes powerful in learning
location-dependent yet view-invariant features with good scalability towards
unseen images of new locations. Compared to the state-of-the-art methods, the
proposed PVDA requires less inference time but has achieved superior
performance on the University-1652 dataset.
