Automatic Geolocation Correction of Satellite Imagery
- 557 Downloads
Modern satellites tag their images with geolocation information using GPS and star tracking systems. Depending on the quality of the geopositioning equipment, errors may range from a few meters to tens of meters on the ground. At the current state of art, there is no established method to automatically correct these errors limiting the large-scale joint utilization of cross-platform satellite images. In this paper, an automatic geolocation correction framework that corrects images from multiple satellites 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 demonstrated through a 3-D surface reconstruction application. The 3-D surface models given by original satellite geopositioning metadata, and the corrected metadata, are compared. The quality difference is measured through an entropy-based metric applied to the orthographic height maps given by the 3-D surface models. Measuring the absolute accuracy of the framework is harder due to lack of publicly available high-precision ground surveys. However, the geolocation 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 metadata to demonstrate the improved quality of alignment.
KeywordsGeoregistration Satellite imagery 3-D modeling RPC camera model Bias correction
Supported by the Intelligence Advanced Research Projects Activity (IARPA) via Air Force Research Laboratory (AFRL), contract FA8650-12-C-7211. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.
The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, AFRL, or the U.S. government.
- Dial, G., & Grodecki, J. (2005). RPC replacement camera models. In Proceedings of the ASPRS 2005 annual conference. Google Scholar
- Mikhail, E. M., Bethel, J. S., & McGlone, J. C. (2001). Introduction to modern photogrammetry. New York: Wiley.Google Scholar
- Oh, J., Toth, C., & Grejner-Brzezinska, D. (2010). Automatic georeferencing of aerial images using high-resolution stereo satellite images. In ASPRS annual conference. Google Scholar
- Pollard, T., & Mundy, J. L. (2007). Change detection in a 3-D world. In Proceedings of computer vision and pattern recognition (CVPR). Google Scholar
- Pritt, M. D., & LaTourette, K. J. (2011). Automated georegistration of motion imagery. In Applied imagery pattern recognition workshop (AIPR). Google Scholar
- Tom, V., Wallace, G. K., & Wolfe, G. J. (1983). Image registration by a statistical method. In Proceedings of SPIE applications of digital image processing VI (p. 432).Google Scholar