High Resolution Satellite Image Orientation Models

  • Mattia Crespi
  • Francesca Fratarcangeli
  • Francesca Giannone
  • Francesca Pieralice


A few years ago high resolution satellite imagery became available to a limited number of government and defense agencies that managed such imagery with highly sophisticated software and hardware tools. Such images became available to civil users in 1999 with the launch of Ikonos, the first civil satellite offering a spatial resolution of 1 m. Since then other high resolution satellites have been launched, among which there are EROS-A (1.8 m), QuickBird (0.61 m), Orbview-3 (1 m), EROS-B (0.7 m), Worldview-1 (0.5 m) and GeoEye-1 (0.41 m), with many others being planned to launch in the near future. High resolution satellite imagery is now available in different formats and processing levels at an affordable price. The diverse types of sensors and their growing availability are revolutionizing the role of satellite imagery in a number of applications, ranging from intelligency to insurance, media, marketing, agriculture, utilities, urban planning, forestry, environmental monitoring, transportation, real estate etc. As a possible alternative to aerial imagery, high resolution satellite imagery has also impact in cartographic applications, such as in orthophoto production, especially for areas where the organization of photogrammetric surveying may be critical.


Root Mean Square Error Global Navigation Satellite System Global Navigation Satellite System Singular Value Decomposition Rigorous Model 
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  1. Beyer HA (1992) Geometric and radiometric analysis of a CCD-Camera based photogrammetric close-range system. PhD thesis, Institut fur Geodasie und Photogrammetry, Nr. 51, ETH, Zurich, May 1992Google Scholar
  2. Bianconi M, Crespi M, Fratarcangeli F, Giannone F, Pieralice F (2008) A new strategy for rational polynomial coefficients generation. Proceeding EARSeL Joint Workshop Remote Sensing, New Challenges of High Resolution, Bochum (Germany) March 5-7 2008Google Scholar
  3. Brovelli M.A, Crespi M, Fratarcangeli F, Giannone F, Realini E (2008) Accuracy assessment of high resolution satellite imagery orientation by leave-one-out method, ISPRS Journal of Photogrammetry and Remote Sensing, Vol.63 Issue 4 pags. 427-440CrossRefGoogle Scholar
  4. Brown DC (1971) Close-range camera calibration. Photogrammetric Engineering. Vol.37, No. 8, pp.855-866Google Scholar
  5. Crespi M, Fratarcangeli F, Giannone F, Pieralice F (2008a) Orientation Of Quickbird, Ikonos and Eros A Stereopairs by an Original Rigorous Model International Calibration and Orientation Workshop. Proceeding of EuroCOW 2008, Castelldefels (Spain) January 30-February 1 2008Google Scholar
  6. Crespi M, Fratarcangeli F, Giannone F, Jacobsen K, Pieralice F (2008b) Orientation of Cartosat-1 Stereo Imagery. Proceeding of EARSeL Joint Workshop Remote Sensing, New Challenges of High Resolution, Bochum (Germany) March 5-7 2008Google Scholar
  7. Elisseeff A, Pontil M (2002) Leave-one-out error and stability of learning algorithms with applications. Advances in Learning Theory: Methods, Models and Applications:111-130. NATO Advanced Study Institute on Learning Theory and PracticeGoogle Scholar
  8. Fraser C S and Hanley H B (2003) Bias compensation in rational functions for Ikonos satellite imagery Photogrammetric Engineering and Remote Sensing, Vol. 69(1), pp. 53-57Google Scholar
  9. Geisser S (1975) The predictive sample reuse method with applications. Journal of the American Statistical Association, Vol. 70, No. 350, pp.320-328CrossRefGoogle Scholar
  10. Giannone F (2006) A rigorous model for High Resolution Satellite Imagery Orientation. Phd Thesis of the Sapienza University of Rome. Supervisors: M. Crespi. Available:
  11. Golub G, Van Loan C F (1993) Matrix computation. The Johns Hopkins University Press, Baltimore and LondonGoogle Scholar
  12. Hanley H B, Fraser C S (2004) Sensor orientation for high-resolution satellite imagery: further insights into bias-compensated RPC, Available:
  13. Hofmann Wellenhof B, Lichtenegger H, Wasle E (2008) GNSS Global Navigation Satellite System, Spinger-Verlag. ISBN: 978-3-211-73012-6Google Scholar
  14. Jacobsen K (1998) Geometric calibration of space remote sensing cameras for efficient processing. IAPRS, Vol. 32, Part 1, pp. 33-43Google Scholar
  15. Kaula WM (1966) Theory of Satellite Geodesy. Blaisedell Publishing CompanyGoogle Scholar
  16. Montenbruck O, Gill E (2001) Satellite orbits. Springer, BerlinGoogle Scholar
  17. Neumaier A (1998) Solving ill-conditioned and singular linear systems: a tutorial on regularization SIAM Review, Issue 3, Vol. 40 pp. 636-666CrossRefGoogle Scholar
  18. NIMA (2000) The Compendium of Controlled Extensions (CE) for the National Imagery Transmission Format (Version 2.1) NITFS technical boardGoogle Scholar
  19. Noerdlinger PD (1999) Atmospheric refraction effects in earth remote sensing. ISPRS Journal of Photogrammetry & Remote Sensing Vol. 54, pp. 360–373CrossRefGoogle Scholar
  20. Pieralice F (2007) Orthorectification of IKONOS High Resolution Satellite Imagery: definition, implementation and accuracy assessment of an original orientation model. Degree thesis of the Sapienza University of Rome. Supervisors: M. Crespi. Not publishedGoogle Scholar
  21. Poli D (2005) Modelling of spaceborne linear array sensors. Diss., Technische Wissenschaften ETH Zurich, Nr. 15894, IGP MitteilungGoogle Scholar
  22. Simon R, Dobbin K, McShane L M (2003) Pitfalls in the Use of DNA Microarray Data for Diagnostic and Prognostic Classification. JNCI Journal of the National Cancer Institute 2003 95(1), 14-18. Oxford University PressCrossRefGoogle Scholar
  23. Stone M (1974) Cross-validatory choice and assessment of statistical predictions (with discussion). Journal of the Royal Statistical Society B, No. 36, pp.111-147Google Scholar
  24. Strang G, Borre K (1997) Linear algebra, Geodesy and GPS. Wellesley-Cambridge Press, WellesleyGoogle Scholar
  25. Tao C V, Hu Y (2001a) The rational function model-A tool for processing high resolution imagery Earth Observation Magazine, Vol. 10 (1), pp. 13-16Google Scholar
  26. Tao C V, Hu Y (2001b) A comprehensive study of the rational function model for photogrammetric processing Photogrammefric Engineering & Remote Sensing, Vol. 67(12), pp. 1347-1357Google Scholar
  27. Tao C V, Hu Y (2001c) Use of the rational function model for image rectification Canadian Journal of Remote Sensing, Vol. 27(6), pp. 593-602Google Scholar
  28. Tao C V and Hu Y (2002) 3D reconstruction methods based on the rational function model. Photogrammetric Engineering &Remote Sensing, vol. 68(7), pp.705-714Google Scholar
  29. Teunissen P.J.G, Kleusberg A (1998) GPS for Geodesy, Springer-Verlag. ISBN: 3-540-63661-7Google Scholar
  30. Toutin T, Chénier R, Carbonneau Y (2000) 3D models for high resolution images: examples with Quickbird, Ikonos and EROS In Proceedings of ISPRS Commission IV Symposium, Joint International Symposium on Geospatial Theory, Processing and Applications, Ottawa, pp. 547-551Google Scholar
  31. Westin T (1990) Precision rectification of SPOT imagery. Photogrammetric Engineering and Remote Sensing Vol.56, n. 2, pp. 247–253Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Mattia Crespi
    • 1
  • Francesca Fratarcangeli
    • 1
  • Francesca Giannone
    • 1
  • Francesca Pieralice
    • 1
  1. 1.Dipartimento Idraulica Trasporti StradeArea di Geodesia e Geomatica, Sapienza Universitá di RomaRomeItaly

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