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Cross-view geo-localization is the problem of estimating the position andorientation (latitude, longitude and azimuth angle) of a camera at ground levelgiven a large-scale database of geo-tagged aerial (e.g., satellite) images.Existing approaches treat the task as a pure location estimation problem bylearning discriminative feature descriptors, but neglect orientation alignment.It is well-recognized that knowing the orientation between ground and aerialimages 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 DynamicSimilarity Matching network to estimate cross-view orientation alignment duringlocalization. In particular, we address the cross-view domain gap by applying apolar transform to the aerial images to approximately align the images up to anunknown azimuth angle. Then, a two-stream convolutional network is used tolearn deep features from the ground and polar-transformed aerial images.Finally, we obtain the orientation by computing the correlation betweencross-view features, which also provides a more accurate measure of featuresimilarity, improving location recall. Experiments on standard datasetsdemonstrate that our method significantly improves state-of-the-artperformance. Remarkably, we improve the top-1 location recall rate on the CVUSAdataset by a factor of 1.5x for panoramas with known orientation, by a factorof 3.3x for panoramas with unknown orientation, and by a factor of 6x for180-degree FoV images with unknown orientation.

Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching