Retrieving relevant multimedia content is one of the main problems in a world
that is increasingly data-driven. With the proliferation of drones, high
quality aerial footage is now available to a wide audience for the first time.
Integrating this footage into applications can enable GPS-less geo-localisation
or location correction.
In this paper, we present an orientation-guided training framework for
UAV-view geo-localisation. Through hierarchical localisation orientations of
the UAV images are estimated in relation to the satellite imagery. We propose a
lightweight prediction module for these pseudo labels which predicts the
orientation between the different views based on the contrastive learned
embeddings. We experimentally demonstrate that this prediction supports the
training and outperforms previous approaches. The extracted pseudo-labels also
enable aligned rotation of the satellite image as augmentation to further
strengthen the generalisation. During inference, we no longer need this
orientation module, which means that no additional computations are required.
We achieve state-of-the-art results on both the University-1652 and
University-160k datasets.

通过建议的方向引导训练框架，本文在无人机成像中通过层级定位，预测方向标签以支持训练，实现了高精度的地理定位，同时还可以增强卫星图像对空间的推广。