Modern satellites tag their images with geo-location information using GPS and star tracking systems. Depending on the quality of the geo-positioning equipment, geo-location errors may range from a few meters to tens of meters on the ground. At the current state of art, there is not an established method to automatically correct these errors limiting the large-scale utilization of the satellite imagery. In this paper, an automatic geo-location correction framework that corrects multiple satellite images simultaneously is presented. As a result of the proposed correction process, all the images are effectively registered to the same absolute geodetic coordinate frame. The usability and the quality of the correction framework are shown through probabilistic 3-D surface model reconstruction. The models given by original satellite geo-positioning meta-data and the corrected meta-data are compared and the quality difference is measured through an entropy-based metric applied onto the high resolution height maps given by the 3-D models. Measuring the absolute accuracy of the framework is harder due to lack of publicly available high precision ground surveys, however, the geo-location of images of exemplar satellites from different parts of the globe are corrected and the road networks given by OpenStreetMap are projected onto the images using original and corrected meta-data to show the improved quality of alignment.
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