Classification of remote sensing data using margin-based ensemble methods

This work exploits the margin theory to design better ensemble classifiers for remote sensing data. The margin paradigm is at the core of a new bagging algorithm. This method increases the classification accuracy, particularly in case of difficult classes, and significantly reduces the training set size. The same margin framework is used to derive a novel ensemble pruning algorithm. This method not only highly reduces the complexity of ensemble methods but also performs better than complete bagging in handling minority classes. Our techniques have been successfully used for the classification of remote sensing data.