30 December 1994 Thematic image segmentation by a concept formation algorithm
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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.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jerzy J. Korczak, Denis Blamont, Alain Ketterlin, "Thematic image segmentation by a concept formation algorithm", Proc. SPIE 2315, Image and Signal Processing for Remote Sensing, (30 December 1994); doi: 10.1117/12.196719; https://doi.org/10.1117/12.196719


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