Abstract
Many image understanding systems rely heavily on a priori knowledge of their domain of application, drawing parallels with and exploiting techniques developed in the wider field of knowledge-based systems (KBSs). Attempts, typified by the KADS/CommonKADS projects, have recently been made to develop a structured, knowledge engineering approach to KBS development. Those working in image understanding, however, continue to employ 151 generation KBS methods. The current paper presents an analysis of existing image understanding systems; specifically those concerned with aerial image interpretation, from a knowledge engineering perspective. Attention is focused on the relationship between the structure of the systems considered and the existing KADS/CommonKADS models of expertise, sometimes called “generic task models”. Map**s are identified between each system and an appropriate task model, identifying common inference structures and use of knowledge.
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References
W.J. Clancey, “Heuristic Classification,” Artificial Intelligence, vol. 27, pp. 215–251,1985
S. J. Cosby and R. Thomas, “IRS: A Hierarchical Knowledge Based System for aerial Image Interpretation,” 3rd International Conference on Industrial Engineering Applications of Artificial Intelligence and Expert Systems, Charleston, SC, USA, July 16–19, 1990
D. Crevier, and R. Lepage, “Knowledge-Based Image Understanding Systems: A Survey,” Computer Vision & Image Understanding, vol. 67, no. 2, pp. 161–185, Aug. 1997
R. D. Ferrant Multi-Spectral Image Analysis System Conference on Artificial Intelligence, Denver, Co, USA, 1984
T. Matsuyama and V. S. Hwang,SIGMA: A Knowledge-Based Aerial Image Understanding System. New York, Plenum Press, 1990
L. Moller-Jensen, “Knowledge-Based Classification of an Urban Area Using Texture and Context Information in Landsat-EM Imagery,”Photogrammetric Engineering & Remote Sensing, vol. 56, no. 6, June 1990, pp. 889–904
G. Schreiber, et. aI.,Knowledge Engineering and Management: The CommonKADS Methodology. Cambridge, Mass.: MIT Press, 1999
H. Murai and S. Omatu, Remote Sensing Image Analysis Using a Neural network & Knowledge-Based Processing International Journal of Remote Sensing, vol. 18, no. 4, May 1997, pp. 811–828
G. Schreiber, B. Wielinga and J. Breuker, (ed.),KADS: A Principled Approach to Knowledge-Based System Development. London: Academic Press Ltd., 1993
D. S. W. Tansley and C. C. Hayball,Knowledge-Based Systems Analysis & Design: A KADS Developer’s Handbook. Hertfordshire, Hemel Hempstead: Prentice Hall International (UK) Ltd., 1993
J. Ton et. al.: “Knowledge-Based Segmentation of Landsat Images”, IEEE Transactions on Geoscience & Remote Sensing, vol. 29, no. 2, March 1991
S. W. Wharton, Spectral-Knowledge-Based Approach for Urban Land-Cover Discrimination, IEEE Transactions on Geoscience & Remote Sensing, vol. 25, no. 3, May 1987, pp. 273–282
B. Wielinga, A. Th. Schreiber and J. A. Breuker, “KADS: A Modeling Approach to Knowledge Engineering,” Knowledge Acquisition, vol. 5, pp. 5–53, 1992
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© 2002 Springer-Verlag London
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Darwish, A., Pridmore, T., Elliman, D. (2002). Interpreting Aerial Images: A Knowledge-Level Analysis. In: Macintosh, A., Moulton, M., Preece, A. (eds) Applications and Innovations in Intelligent Systems IX. Springer, London. https://doi.org/10.1007/978-1-4471-0149-9_13
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DOI: https://doi.org/10.1007/978-1-4471-0149-9_13
Publisher Name: Springer, London
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