Orthographic Stereo Correlator on the Terrain Model for Apollo Metric Images

  • Taemin Kim
  • Kyle Husmann
  • Zachary Moratto
  • Ara V. Nefian
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6938)


A stereo correlation method on the object domain is proposed to generate the accurate and dense Digital Elevation Models (DEMs) from lunar orbital imagery. The NASA Ames Intelligent Robotics Group (IRG) aims to produce high-quality terrain reconstructions of the Moon from Apollo Metric Camera (AMC) data. In particular, IRG makes use of a stereo vision process, the Ames Stereo Pipeline (ASP), to automatically generate DEMs from consecutive AMC image pairs. Given camera parameters of an image pair from bundle adjustment in ASP, a correlation window is defined on the terrain with the predefined surface normal of a post rather than image domain. The squared error of back-projected images on the local terrain is minimized with respect to the post elevation. This single dimensional optimization is solved efficiently and improves the accuracy of the elevation estimate.


Image Pair Stereo Match Bundle Adjustment Normalize Cross Correlation Correlation Window 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Noble, S.K., et al.: The Lunar Mapping and Modeling Project. LPI Contributions 1515, 48 (2009)Google Scholar
  2. 2.
    Mordohai, P., Medioni, G.: Dense multiple view stereo with general camera placement using tensor voting. In: IEEE 3DPVT, pp. 725–732 (2004)Google Scholar
  3. 3.
    Thompson, C.M.: Robust photo-topography by fusing shape-from-shading and stereo, Massachusetts Institute of Technology (1992)Google Scholar
  4. 4.
    Price, K.: Annotated computer vision bibliography. Institute for Robotic and Intelligent System School of Engineering (IRIS), University of Southern California (1995)Google Scholar
  5. 5.
    Heipke, C., Piechullek, C.: Toward surface reconstruction using multi-image shape from shading. In: ISPRS, pp. 361–369 (1994)Google Scholar
  6. 6.
    Cryer, J.E., Tsai, P.S., Shah, M.: Integration of shape from shading and stereo. Pattern recognition 28(7), 1033–1043 (1995)CrossRefGoogle Scholar
  7. 7.
    Hapke, B.: Bidirectional reflectance spectroscopy, 1, Theory. J. Geophys. Res. 86, 3039–3054 (1981)CrossRefGoogle Scholar
  8. 8.
    Minnaert, M.: Photometry of the Moon. Planets and Satellites, 213 (1961)Google Scholar
  9. 9.
    Buratti, B., Veverka, J.: Voyager photometry of Rhea, Dione, Tethys, Enceladus and Mimas. Icarus 58(2), 254–264 (1984)CrossRefGoogle Scholar
  10. 10.
    Broxton, M.J., et al.: The Ames Stereo Pipeline: NASA’s Open Source Automated Stereogrammetry Software, NASA Ames Research Center (2009)Google Scholar
  11. 11.
    Menard, C.: Robust Stereo and Adaptive Matching in Correlation Scale-Space, Institute of Automation, Vienna Institute of Technology (1997)Google Scholar
  12. 12.
    Sun, C.: Fast stereo matching using rectangular subregioning and 3D maximum-surface techniques. International Journal of Computer Vision 47(1), 99–117 (2002)CrossRefzbMATHGoogle Scholar
  13. 13.
    Nefian, A., et al.: A Bayesian Formulation for Subpixel Refinement in Stereo Orbital Imagery. In: International Conference on Image Processing, Cairo, Egypt (2009)Google Scholar
  14. 14.
    Bay, H., et al.: Speeded-up robust features (SURF). Computer Vision and Image Understanding 110(3), 346–359 (2008)CrossRefGoogle Scholar
  15. 15.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Commun. Assoc. Comp. Mach. 24(6), 381–395 (1981)MathSciNetGoogle Scholar
  16. 16.
    Triggs, B., et al.: Bundle adjustment - a modern synthesis. In: Triggs, B., Zisserman, A., Szeliski, R. (eds.) ICCV-WS 1999. LNCS, vol. 1883, pp. 298–372. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  17. 17.
    Hartley, R., Zisserman, A.: Multiple view geometry in computer vision. Cambridge University Press, Cambridge (2003)zbMATHGoogle Scholar
  18. 18.
    Kim, T., Moratto, Z., Nefian, A.V.: Robust Mosaicking of Stereo Digital Elevation Models from the Ames Stereo PipelineGoogle Scholar
  19. 19.
    Pedersini, F., Pigazzini, P., Sarti, A., Tubaro, S.: 3D Area Matching with Arbitrary Multiview Geometry. In: EURASIP Signal Processing: Image Communication - Special Issue on 3D Video Technology, vol. 14(1-2), pp. 71–94. Elsevier, Amsterdam (October 1998)Google Scholar
  20. 20.
    Broxton, M.J., Nefian, A.V., Moratto, Z., Kim, T., Lundy, M., Segal, A.V.: 3D lunar terrain reconstruction from apollo images. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Kuno, Y., Wang, J., Wang, J.-X., Wang, J., Pajarola, R., Lindstrom, P., Hinkenjann, A., Encarnação, M.L., Silva, C.T., Coming, D. (eds.) ISVC 2009. LNCS, vol. 5875, pp. 710–719. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  21. 21.
    Nefian, A., Husmann, K., Broxton, M., To, V., Lundy, M., Hancher, M.: A Bayesian Formulation for Sub-pixel Refinement in Stereo Orbital Imagery. In: Proceedings of the 2009 IEEE ICIP (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Taemin Kim
    • 1
  • Kyle Husmann
    • 1
  • Zachary Moratto
    • 1
  • Ara V. Nefian
    • 1
    • 2
  1. 1.NASA Ames Research CenterMoffett FieldUSA
  2. 2.Carnegie Mellon UniversityUSA

Personalised recommendations