Peter Pesti, Jeremy Elson, Jon Howell, Drew Steedly, and Matt Uyttendaele
Commercial aerial imagery websites, such as Google Maps, MapQuest, Microsoft Virtual Earth, and Yahoo! Maps, provide high-quality seamless orthographic imagery for many populated areas, employing sophisticated equipment and proprietary image postprocessing pipelines. There are many areas of the world with poor coverage where locals might benefit from recent, high-resolution orthographic imagery, but which do not fit into the schedules and scaling model of the big sites.
This paper describes MapStitcher, a system that orthorectifies and geographically registers imagery using only low-cost capturing equipment. MapStitcher combines manually-entered relationships between images and known ground references with a MOPs-based image-stitching technique that automatically discovers image-to-image relationships. Our image registration pipeline first extracts and matches feature points, then clusters images, then uses RANSAC-initialized bundle adjustment to simultaneously optimize all constraints over the entire image set. Simultaneous optimization balances the requirements of precise stitching and absolute placement accuracy. We used this technique to image a portion of the Skagit River Valley in the vicinity of the town of Concrete, WA (pop. 790) at 0.15 m/pixel. Our technique is more accurate than stitching followed by “rubber-sheeting” (deforming the stitched image into global coordinates), while it also requires less effort and produces a better-stitched composite than rubber-sheeting images separately.
In Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS)
Publisher Association for Computing Machinery, Inc.
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