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Semi-Automatic Analysis of High-Resolution Satellite Images

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Machine Vision and Advanced Image Processing in Remote Sensing
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Summary

In this chapter we present a semi-automatic scene analysis system. The image interpretation knowledge that must be integrated in the scene analysissystem in order to regularize the otherwise ill-posed scene analysis problem, its representation and integration will be analyzed. Finally, the results of a prototype system will be presented.

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References

  1. D. Borghys, C. Perneel, W. Mees, and M. Acheroy, “STANAG 3596 based multispectral information system - feasibility study”, Technical Report, Royal Military Academy - Signal & Image Centre, Brussels (Belgium), 1996.

    Google Scholar 

  2. R.A. Brooks, “Symbolic reasoning among 3-D models and 2-D images”, Artificial Intelligence Journal, vol. 17, pp. 285–348, 1981.

    Article  Google Scholar 

  3. R.A. Brooks, “Model-based three-dimensional interpretations of two-dimensional images”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 5, pp. 140–150, March 1983.

    Article  Google Scholar 

  4. J. Furnkranz, “The role of qualitative knowledge in machine learning”, Technical Report, Austrian Research Institute for Artificial Intelligence, Vienna (Austria), November 1992.

    Google Scholar 

  5. O. Grau and R. Tonjes, “Knowledge based modelling of natural scenes”, in European Workshop on Combined Real and Synthetic Image Processing for Broadcast and Video Production, (Hamburg (Germany)), 23–24 November 1994.

    Google Scholar 

  6. K.N. Leibovic, Science of Vision. New York: Springer-Verlag, 1991.

    Google Scholar 

  7. D.M. McKeown, W.A. Harvey, and J. McDermott, “Rule-based interpretation of aerial imagery”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 7, pp. 570–585, September 1985.

    Article  Google Scholar 

  8. D.M. McKeown, W.A. Harvey, and L.E. Wixson, “Automating knowledge acquisition for aerial image interpretation”, Computer Vision Graphics and Image Processing, vol. 46, pp. 37–81, 1989.

    Article  Google Scholar 

  9. W. Mees, “Scene analysis: fusing image processing and artificial intelligence”, IEEE-Computer Society’s Student Newsletter, looking forward, vol. 5, pp. 5–8, Spring 1997.

    Google Scholar 

  10. W. Mees, “Representing a fuzzy production rule system using a high-level Petri net graph”, in Proceedings of the The Seventh Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU’98, vol. II, (Paris (France)), pp. 1880-1883, 6-10 July 1998.

    Google Scholar 

  11. W. Mees and M. Acheroy, “Automated interpretation of aerial photographs using local and global rules”, in Fuzzy logic and evolutionary programming (C.H. Dagli, M. Akay, C.L.P. Chen, B.R. Fernandez, and J. Ghosh, eds.), vol. 5 of ASME Press series on intelligent engineering systems through artificial neural networks, (St. Louis, MI (USA)), pp. 459–465, 12 15 November 1995.

    Google Scholar 

  12. W. Mees and C. Perneel, “Advances in computer assisted image interpretation”, Informatica - International Journal of Computing and Informatics, vol. 22, pp. 231–243, May 1998.

    Google Scholar 

  13. M. Nagao, T. Matsuyama, and H. Mori, “Structures analysis of complex aerial photographs”, in International Joint Conference on Artificial Intelligence, JCAI-79, (Tokyo, Japan), pp. 610-616, 1979.

    Google Scholar 

  14. H. Niemann, G. Sagerer, S. Schröder, and F. Kummert, “ERNEST: A semantic network system for pattern understanding”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, pp. 883–905, September 1990.

    Article  Google Scholar 

  15. H. Penny Nii, “Blackboard systems part I: The blackboard model of problem solving and the evolution of blackboard architectures”, The Al Magazine, pp. 38-53, summer 1986.

    Google Scholar 

  16. H. Penny Nii, “Blackboard systems part II: Blackboard application systems, blackboard systems from a knowledge engineering perspective”, The Al Magazine, pp. 82-106, summer 1986.

    Google Scholar 

  17. A.P. Pentland, “Perceptual organization and the representation of natural form”, in Readings in Computer Vision, pp. 680-699, Morgan Kaufmann Publishers Inc., 1987.

    Google Scholar 

  18. T. Poggio, V. Torre, and C. Koch, “Computational vision and regularization theory”, in Readings in Computer Vision, pp. 638 -643, Morgan Kaufmann Publishers Inc., 1987.

    Google Scholar 

  19. H. Reichgelt, Knowledge Representation: An Al Perspective. Ablex Publishing Corporation, 1990.

    Google Scholar 

  20. I. Tannous, S. Gobert, T. Laurengot, J.-M. Dulac, and O. Goretta, “Design of a multi-sensor system for 3D site model acquisition and exploitation”, in Multi- Sensor Systems and Data Fusion for Telecommunications, Remote Sensing and Radar, no. CP-595 in AGARD, (Lisbon (Portugal)), pp. 4.1-4.10, The Sensor and Propagation Panel Symposium, 29 September 2 October 1997.

    Google Scholar 

  21. A.C. van den Broeck, P. Hoogeboom, and M. van Persie, “Multi-sensor remote sensing for military cartography”, in Multi-Sensor Systems and Data Fusion for Telecommunications, Remote Sensing and Radar, no. CP-595 in AGARD, (Lisbon (Portugal)), pp. 3.1-3.7, The Sensor and Propagation Panel Symposium, 29 September 2 October 1997.

    Google Scholar 

  22. P.H. Winston, Artificial Intelligence. Addison-Wesley, second edition, 1984.

    Google Scholar 

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© 1999 Springer-Verlag Berlin · Heidelberg

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Mees, W., Acheroy, M. (1999). Semi-Automatic Analysis of High-Resolution Satellite Images. In: Kanellopoulos, I., Wilkinson, G.G., Moons, T. (eds) Machine Vision and Advanced Image Processing in Remote Sensing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60105-7_22

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  • DOI: https://doi.org/10.1007/978-3-642-60105-7_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-64260-9

  • Online ISBN: 978-3-642-60105-7

  • eBook Packages: Springer Book Archive

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