  Cross-view geo-localization is the problem of estimating the position and
orientation (latitude, longitude and azimuth angle) of a camera at ground level
given a large-scale database of geo-tagged aerial (e.g., satellite) images.
Existing approaches treat the task as a pure location estimation problem by
learning discriminative feature descriptors, but neglect orientation alignment.
It is well-recognized that knowing the orientation between ground and aerial
images can significantly reduce matching ambiguity between these two views,
especially when the ground-level images have a limited Field of View (FoV)
instead of a full field-of-view panorama. Therefore, we design a Dynamic
Similarity Matching network to estimate cross-view orientation alignment during
localization. In particular, we address the cross-view domain gap by applying a
polar transform to the aerial images to approximately align the images up to an
unknown azimuth angle. Then, a two-stream convolutional network is used to
learn deep features from the ground and polar-transformed aerial images.
Finally, we obtain the orientation by computing the correlation between
cross-view features, which also provides a more accurate measure of feature
similarity, improving location recall. Experiments on standard datasets
demonstrate that our method significantly improves state-of-the-art
performance. Remarkably, we improve the top-1 location recall rate on the CVUSA
dataset by a factor of 1.5x for panoramas with known orientation, by a factor
of 3.3x for panoramas with unknown orientation, and by a factor of 6x for
180-degree FoV images with unknown orientation.
