Novel Semi-Supervised Land Cover Classification Technique of Remotely Sensed Images

This research article addresses the problem of land-cover classification from the multi-spectral remotely sensed images using a novel self-training based semi-supervised learning (SSL) technique. The proposed system, instead of using a single classifier, builds an ensemble of classifiers with the hope that the ensemble system will have a lesser generalization error than any of its members. Each component classifier is trained independently using the proposed self-training approach on different training sub-sets and their predictions on test samples are combined using a new instant run-off voting (IRV) based classifier combination method. Each training subset consists of a few labeled samples and a comparatively large number of unlabeled data items. The cluster-and-label method for self-training has been adopted here considering the support vector machines (SVM) as the supervised learner and minimum spanning tree based clustering. A normalized histogram intersection kernel has been proposed which has shown to outperform the state-of-the-art kernel functions in terms of the SVM generalization accuracy. A novel cluster validity index specifically for graph based clustering has also been introduced to access the quality of the clustering. The proposed training method has the advantage that it can find classes for which the labeled training data are unavailable initially. A number of multi-spectral images have been considered for the experimental evaluation and a comparison with some of the well-established techniques from the literature has confirmed the superiority of the proposed classifier system.