Bayesian combination method of classifiers for cluster validation problem

Salim Chitroub; Amrane Houacine; Boualem Sansal

doi:10.1117/12.413894

19 January 2001 Bayesian combination method of classifiers for cluster validation problem

Salim Chitroub, Amrane Houacine, Boualem Sansal

Proceedings Volume 4170, Image and Signal Processing for Remote Sensing VI; (2001) https://doi.org/10.1117/12.413894
Event: Europto Remote Sensing, 2000, Barcelona, Spain

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.

Citation Download Citation

Salim Chitroub, Amrane Houacine, and Boualem Sansal "Bayesian combination method of classifiers for cluster validation problem", Proc. SPIE 4170, Image and Signal Processing for Remote Sensing VI, (19 January 2001); https://doi.org/10.1117/12.413894

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