  Ensemble classification is an emerging approach to land cover mapping whereby
the final classification output is a result of a consensus of classifiers.
Intuitively, an ensemble system should consist of base classifiers which are
diverse i.e. classifiers whose decision boundaries err differently. In this
paper ensemble feature selection is used to impose diversity in ensembles. The
features of the constituent base classifiers for each ensemble were created
through an exhaustive search algorithm using different separability indices.
For each ensemble, the classification accuracy was derived as well as a
diversity measure purported to give a measure of the inensemble diversity. The
correlation between ensemble classification accuracy and diversity measure was
determined to establish the interplay between the two variables. From the
findings of this paper, diversity measures as currently formulated do not
provide an adequate means upon which to constitute ensembles for land cover
mapping.
