Low-cost orthographic imagery

ABSTRACT

Commercial aerial imagery websites, such as Google Maps, MapQuest, Microsoft Virtual Earth, and Yahoo! Maps, provide high- 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.

REFERENCES

• 1: M. Brown , D. G. Lowe, Unsupervised 3D Object Recognition and Reconstruction in Unordered Datasets, Proceedings of the Fifth International Conference on 3-D Digital Imaging and Modeling, p.56-63, June 13-16, 2005 [doi>10.1109/3DIM.2005.81]
• 2: Matthew Brown , David G. Lowe, Automatic Panoramic Image Stitching using Invariant Features, International Journal of Computer Vision, v.74 n.1, p.59-73, August 2007 [doi>10.1007/s11263-006-0002-3]
• 3: Matthew Brown , Richard Szeliski , Simon Winder, Multi-Image Matching Using Multi-Scale Oriented Patches, Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1, p.510-517, June 20-26, 2005 [doi>10.1109/CVPR.2005.235]
• 4: Bon A. DeWitt , Paul R. Wolf, Elements of Photogrammetry(with Applications in GIS), McGraw-Hill Higher Education, 2000
• 5: Jeremy Elson , Jon Howell , John R. Douceur, MapCruncher: integrating the world's geographic information, ACM SIGOPS Operating Systems Review, v.41 n.2, p.50-59, April 2007 [doi>10.1145/1243418.1243425]
• 6: Martin A. Fischler , Robert C. Bolles, Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, v.24 n.6, p.381-395, June 1981 [doi>10.1145/358669.358692]
• 7: Richard Hartley , Andrew Zisserman, Multiple view geometry in computer visiond, Cambridge University Press, New York, NY, 2001
• 8: F. A. V. D. Heuvel. Exterior orientation using coplanar parallel lines. Proceedings of the 10th Scandinavian Conference on Image Analysis, pages 71--78, 1997.
• 9: F. A. V. D. Heuvel. Estimation of interior orientation parameters from constraints on line measurements in a single image. International Archives of Photogrammetry and Remote Sensing, 32:81--88, 1999.
• 10: Vivek Kwatra , Arno Schödl , Irfan Essa , Greg Turk , Aaron Bobick, Graphcut textures: image and video synthesis using graph cuts, ACM Transactions on Graphics (TOG), v.22 n.3, July 2003
• 11: S. Mahamud, M. Hebert, Y. Omori, and J. Ponce. Provably-convergent iterative methods for projective structure from motion. In CVPR, pages 1018--1025. IEEE Computer Society, 2001.
• 12: P. F. McLauchlan and A. Jaenicke. Image mosaicing using sequential bundle adjustment. Image Vision Comput, 20(9--10):751--759, 2002.
• 13: Long Quan , Zhongdan Lan, Linear N-Point Camera Pose Determination, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.21 n.8, p.774-780, August 1999 [doi>10.1109/34.784291]
• 14: B. Vandeportaele, C. Dehais, M. Cattoen, and P. Marthon. ORIENT-CAM, A camera that knows its orientation and some applications. In J. F. M. Trinidad, J. A. Carrasco-Ochoa, and J. Kittler, editors, CIAPR, volume 4225 of Lecture Notes in Computer Science, pages 267--276. Springer, 2006.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.4 IMAGE PROCESSING AND COMPUTER VISION

Additional Classification:
  J. Computer Applications
  J.2 PHYSICAL SCIENCES AND ENGINEERING

Keywords:
aerial photography, automatic georegistration, feature matching, image stitching, mapping, photogrammetry