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Ensemble classification is an emerging approach to land cover mapping wherebythe final classification output is a result of a consensus of classifiers.Intuitively, an ensemble system should consist of base classifiers which arediverse i.e. classifiers whose decision boundaries err differently. In thispaper ensemble feature selection is used to impose diversity in ensembles. Thefeatures of the constituent base classifiers for each ensemble were createdthrough an exhaustive search algorithm using different separability indices.For each ensemble, the classification accuracy was derived as well as adiversity measure purported to give a measure of the inensemble diversity. Thecorrelation between ensemble classification accuracy and diversity measure wasdetermined to establish the interplay between the two variables. From thefindings of this paper, diversity measures as currently formulated do notprovide an adequate means upon which to constitute ensembles for land covermapping.

Land Cover Mapping Using Ensemble Feature Selection Methods