Knowledge-Based Multi-Spectral Image Classification

Mark J. Carlotto; Victor T. Tom; Paul W. Baim; Richard A. Upton

doi:10.1117/12.944845

4 December 1984 Knowledge-Based Multi-Spectral Image Classification

Mark J. Carlotto, Victor T. Tom, Paul W. Baim, Richard A. Upton

Proceedings Volume 0504, Applications of Digital Image Processing VII; (1984) https://doi.org/10.1117/12.944845
Event: 28th Annual Technical Symposium, 1984, San Diego, United States

Abstract

A new approach to the problem of classifying surface materials in satellite multi-spectral imagery is described and demonstrated in this paper. Surface material classes are defined heuristically using rules which describe the typical appearance of the material under specified conditions in terms of relative image measures. A knowledge-based approach allows expert knowledge of the domain to be used directly to develop classification rules. An expert system is currently being developed in the Zetalisp/Flavors programming environment on the Symbolics 3600 Lisp Machine. An example of its use in classifying Landsat Thematic Mapper imagery is presented.

Citation Download Citation

Mark J. Carlotto, Victor T. Tom, Paul W. Baim, and Richard A. Upton "Knowledge-Based Multi-Spectral Image Classification", Proc. SPIE 0504, Applications of Digital Image Processing VII, (4 December 1984); https://doi.org/10.1117/12.944845

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