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Automatic DEM Generation from Discrete Points in Multiple Images

  • H-G. Maas
Part of the International Centre for Mechanical Sciences book series (CISM, volume 365)

Abstract

One of the major difficulties in automatic DEM generation is the procurement of approximate values. In this presentation a new development based on the extraction of discrete points by an interest-operator and epipolar line intersection techniques in multiple overlapping images will be shown. The method can be considered an extension of the well-known MATCH-T approach from a stereo technique to a multi-image technique based on image quadruplets in a 60/60 block or even 6 images of a 80/60 block, and it solves the problem of provision of approximate values inherently. In contrast to most stereo-based techniques, the approach is not based on image pyramids, but on the consequent exploitation of the geometric strength of multiple images, implemented via the intersection of epipolar lines. Although not outlined for a complete DEM generation yet, the method may be very valuable for a hypothesis-free generation of good approximate values for other (e.g. area-based) DEM generation techniques or for the refinement of DEMs degraded by smoothing effects.

Keywords

Digital Elevation Model Object Space Multiple Image Digital Terrain Model Epipolar Line 
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.

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References

  1. 1.
    Ackermann, F., 1994: Digital elevation models–techniques and application, quality standards, development. IAPRS Vol. 30, Part IV, pp. 421–432Google Scholar
  2. 2.
    Baltsavias, M., Stallmann, D., 1992: Advancement in matching of SPOT images by integration of sensor geometry and treatment of radiometric differences. IAPRS Vol. XXIX, Part B4, pp. 916–924Google Scholar
  3. 3.
    Dold, J., Maas, H.-G., 1994: An application of epipolar line intersection in a hybrid close range photogrammetric system. IAPRS Vol. 30, Part VGoogle Scholar
  4. 4.
    Förstner W. (1986): A Feature Based Correspondence Algorithm for Image Matching. IAPRS, Vol. 26, Part 3 /3, pp. 150–166Google Scholar
  5. 5.
    Grün, A., 1985: Adaptive least squares correlation: A powerful image matching technique. South African Journal of Photogrammetry, Remote Sensing and Cartography 14 (3), pp. 175–187Google Scholar
  6. 6.
    Krzystek, P., 1991: Fully automatic measurement of digital terrain models. Proc. of the 43. Photogrammetric Week, Stuttgart, Germany, pp. 203–214Google Scholar
  7. 7.
    Maas, H.-G., 1991: Digital Photogrammetry for Determination of Tracer Particle Coordinates in Turbulent Flow Research. Photogrammetric Engineering & Remote Sensing Vol. 57, No. 12, pp. 1593–1597Google Scholar
  8. 8.
    Maas, H.-G., 1992a: Complexity analysis for the determination of image correspondences in dense spatial target fields. IAPRS, Vol. XXIX, Part B5, pp. 482–485Google Scholar
  9. 9.
    Maas, H.-G., 1992b: Robust Automatic Surface Reconstruction with Structured Light. IAPRS, Vol. XXIX, Part B5, pp. 102–107Google Scholar
  10. 10.
    Schewe, H., 1987: Automatic Photogrammetric Car-Body Measurement. Proc. of the 41. Photogrammetric Week, Stuttgart, Germany, pp. 47–57Google Scholar
  11. 11.
    Ursem, R., 1994: Accurate Reconstruction of a 3D Wireframe of a Human Head. Diploma Thesis TU Delft, The NetherlandsGoogle Scholar

Copyright information

© Springer-Verlag Wien 1996

Authors and Affiliations

  • H-G. Maas
    • 1
  1. 1.ETH ZurichZurichSwitzerland

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