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Proceedings Paper

Thematic image segmentation by a concept formation algorithm
Author(s): Jerzy J. Korczak; Denis Blamont; Alain Ketterlin
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Paper Abstract

Unsupervised empirical machine learning algorithms aim at discovering useful concepts in a stream of unclassified data. Since image segmentation is a particular instance of the problem addressed by these methods, one of these algorithms has been employed to automatically segment remote-sensing images. The region under study is Nepalese Himalayas. Because of important variations in altitude, effects of lighting conditions are multiplied, and the image becomes a very complex object. The behavior of the clustering algorithm is studied on such data. Because of the hierarchical organization of the resulting classes, the segmentation produced may be interpreted in a variety of thematic mappings, depending on the desired level of detail. Experimental results prove the influence of lighting conditions, but also demonstrate very good accuracy on sectors of the image where lighting in almost homogenous.

Paper Details

Date Published: 30 December 1994
PDF: 11 pages
Proc. SPIE 2315, Image and Signal Processing for Remote Sensing, (30 December 1994); doi: 10.1117/12.196719
Show Author Affiliations
Jerzy J. Korczak, Univ. Louis Pasteur (France)
Denis Blamont, Univ. Louis Pasteur (France)
Alain Ketterlin, Univ. Louis Pasteur (France)

Published in SPIE Proceedings Vol. 2315:
Image and Signal Processing for Remote Sensing
Jacky Desachy, Editor(s)

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