Abstract
In a knowledge based classification, ancillary data and knowledge are combined with spectral information. A method of knowledge based classification based on temporal relationships between classes is introduced. Knowledge about crop rotations is represented by means of state transition matrices. Spectral image information, information stored in a geographic information system and knowledge as represented in a matrix are combined in a Bayesian maximum likelihood classification. This method is elaborated for atest area in The Netherlands. Depending on the spectral separation of the classes and the level of detail of the transition matrices, the overall accuracy of the classification increased by 4 to 20% with respect to the result based on only spectral information.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Argialas, D.P. & Harlow, CA., 1990: Computational Image Interpretation Models: An Overview and a Perspective. Photogrammetric Engineering and Remote Sensing, Vol. 56, No. 6, June 1990, pp 871–886.
Desachy J., Debord, P. & Castan, S., 1988: An expert system for satellite image interpretation and GIS based on problem solving. ISPRS proceedings Kyoto, Comm. 4/5, Vol 27, B4.
Fletcher, T.J, 1972: Linear Algebra through its applications. Van Nostrand Reinhold Company, London.
Hepner, G.F., & Logan, T., Ritter, N. & Bryant, N., 1990: Artificial Neural Network Classification using a Minimal Training Set: Comparison to Conventional Supervised Classification. Photogrammetric Engineering and Remote Sensing, Vol. 56, No. 4, pp 469–473.
Kenk, E., Sondheim, M. & Yee, B., 1988: Methods for improving accuracy of thematic mapper ground-cover classifications. Can. Jnl of RS, Vol 14, No 1.
Laan, van der, F.B., 1988: Improvement of classification results of satellite imagery using context information from a topographic map. Proc. ISPRS, Symp. Comm. VII, Kyoto, pp 98–108.
Mather, P.M., 1987: Computer processing of Remotely-Sensed Images. An Introduction. John Wiley & Sons, Chichester.
Middelkoop, H., Miltenburg, J.W, & Mulder, N.J., 1989: Knowledge engineering for image interpretation and classification: a trial run. ITC Journal 1989-1, pp 27–32.
Mulder, N.J., 1985: Classification and decision-making. Photogrammetria 40, pp 95–116.
PAGV, 1989: Bouwplan en vruchtopeenvolging. PAGV publicatie nr. 44, Lelystad. (in Dutch).
RIJP, 1988: Overzicht van de gewassen keuze op de landbouwbedrijven in Oostelijk Flevoland, tijdreeks 1965–1985. RIJP rapport 1988 — 30 cbw, Lelystad. (in Dutch).
Shrinivasan A. & Richards, J.A., 1990, Knowledge-based techniques for multi-source classification. International Journal of Remote Sensing, Vol. 11, No. 3, pp 505–525.
Simonett, D.S., Eagleman, J.E., Erhart, A.B., Rhodes, D.C. & Schwartz, D.E., 1967: The potential of radar as a remote sensor in agriculture: 1. a study with the K-band imagery in West-Kansas. CRES Report No. 16–21, University of Kansas, Lawrence.
Skidmore, A.K., 1989: An Expert System Classifies Eucalypt Forest Types using Thematic Mapper Data and a Digital Terrain Model. Photogrammetric Engineering & Remote Sensing, Vol. 10, No. 1, pp 133–146.
Strahler, A. H., 1980: The use of prior probabilities in maximum likelihood classification of remotely sensed data. Remote Sensing of Environment, 10, pp 135–163.
Swain, P.H., 1978: Bayesian classification in a time-varying environment. IEEE transactions on Systems, Man and Cybernetics, Vol.SMC-8, No. 12, pp 879–883.
Wu, J.K., Cheng, D.S, Wang, W.T. & Cai, D.L., 1988: Model Based remotely sensed imagery interpretation. International Journal of Remote Sensing, Vol. 9, No. 8, pp 1347–1356.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1992 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Middelkoop, H., Janssen, L.L.F. (1992). Knowledge-Based Image Classification. In: Günther, O., Radermacher, F.J., Kuhn, H., Mayer-Föll, R. (eds) Konzeption und Einsatz von Umweltinformationssystemen. Informatik-Fachberichte, vol 301. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-77296-2_22
Download citation
DOI: https://doi.org/10.1007/978-3-642-77296-2_22
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-55158-4
Online ISBN: 978-3-642-77296-2
eBook Packages: Springer Book Archive