We address the problem of ground-to-satellite image geo-localization, that
is, estimating the camera latitude, longitude and orientation (azimuth angle)
by matching a query image captured at the ground level against a large-scale
database with geotagged satellite images. Our prior arts treat the above task
as pure image retrieval by selecting the most similar satellite reference image
matching the ground-level query image. However, such an approach often produces
coarse location estimates because the geotag of the retrieved satellite image
only corresponds to the image center while the ground camera can be located at
any point within the image. To further consolidate our prior research findings,
we present a novel geometry-aware geo-localization method. Our new method is
able to achieve the fine-grained location of a query image, up to pixel size
precision of the satellite image, once its coarse location and orientation have
been determined. Moreover, we propose a new geometry-aware image retrieval
pipeline to improve the coarse localization accuracy. Apart from a polar
transform in our conference work, this new pipeline also maps satellite image
pixels to the ground-level plane in the ground-view via a geometry-constrained
projective transform to emphasize informative regions, such as road structures,
for cross-view geo-localization. Extensive quantitative and qualitative
experiments demonstrate the effectiveness of our newly proposed framework. We
also significantly improve the performance of coarse localization results
compared to the state-of-the-art in terms of location recalls.

本文提出了一种基于几何感知的地球到卫星图像地理定位方法，该方法可以实现对查询图像的精准定位，其精度可达卫星图像的像素级，同时提出了一种新的基于几何感知的图像检索流程，以提高定位准确性。