Model-based active learning for SVM classification of remote sensing images

In this work, we present a new support vector machine (SVM)-based active learning method for the classification of remote sensing images. Starting from an initial suboptimal training set, an iterative process defines the regions of significance in the feature space, then selects additional samples from a large set of unlabeled data and adds them to the training set after their manual labeling. Experimental results on a very high resolution (VHR) image show that the proposed method exhibits promising capabilities to select samples that are really significant for the classification problem, both in terms of accuracy and stability.