A Novel SOM-SVM-Based Active Learning Technique for Remote Sensing Image Classification

In this paper, a novel iterative active learning technique based on self-organizing map (SOM) neural network and support vector machine (SVM) classifier is presented. The technique exploits the properties of the SVM classifier and of the SOM neural network to identify uncertain and diverse samples, to include in the training set. It selects uncertain samples from low-density regions of the feature space by exploiting the topological properties of the SOM. This results in a fast convergence also when the available initial training samples are poor. The effectiveness of the proposed method is assessed by comparing it with several methods existing in the literature using a toy data set and a color image as well as real multispectral and hyperspectral remote sensing images.