An ensemble-driven k-NN approach to ill-posed classification problems
This paper addresses the supervised classification of remote-sensing images in problems characterized by relatively small-size training sets with respect to the input feature space and the number of classifier parameters (ill-posed classification problems). An ensemble-driven approach based on the k-nearest neighbor (k-NN) classification technique is proposed. This approach effectively exploits semilabeled samples (i.e., original unlabeled samples labeled by the classification process) to increase the accuracy of the classification process. Experimental results obtained on ill-posed classification problems confirm the effectiveness of the proposed approach, which significantly increases both the accuracy and the reliability of classification maps.
ill-posed classification problems semilabeled samples semisupervised classification ensemble methods k-nearest neighbor technique automatic classification remote sensing