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Proceedings Paper

Bayesian combination method of classifiers for cluster validation problem
Author(s): Salim Chitroub; Amrane Houacine; Boualem Sansal
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Paper Abstract

As a rigorous mathematical formulation of the correspondence technique given in1 and inspired from the data fusion methods, a new approach to detect the robust estimated number of clusters is proposed in this paper. The idea is to make a correspondence between clusters of different classification results obtained with different numbers of clusters, which are superior or equal to the number of land-cover classes. To formulate this idea in a rigorous mathematical framework, we consider the classification results as classifiers we want to combine to obtain the more precise classification result. The combination procedure used is inspired from the recent development in artificial intelligence methods of classifiers combination. Since the Bayesian method uses more information on classifiers in the combination of their results; we have adopted this method in the elaboration of our classifiers combination approach. We demonstrate our methodology by classifying real SAR data provided by the SIR-C sensors.

Paper Details

Date Published: 19 January 2001
PDF: 6 pages
Proc. SPIE 4170, Image and Signal Processing for Remote Sensing VI, (19 January 2001); doi: 10.1117/12.413894
Show Author Affiliations
Salim Chitroub, Univ. of Sciences and Technology of Houari Boumediene (Algeria)
Amrane Houacine, Univ. of Sciences and Technology of Houari Boumediene (Algeria)
Boualem Sansal, Univ. of Sciences and Technology of Houari Boumediene (Algeria)


Published in SPIE Proceedings Vol. 4170:
Image and Signal Processing for Remote Sensing VI
Sebastiano Bruno Serpico, Editor(s)

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