A NOVEL SOM-BASED ACTIVE LEARNING TECHNIQUE FOR CLASSIFICATION OF REMOTE SENSING IMAGES WITH SVM

摘要

This paper presents a novel batch mode active learning technique for solving remote sensing image classification problems. The proposed technique incorporates uncertainty, diversity and cluster assumption criteria to design the query function. The uncertainty criterion is implemented by taking into account the properties of the support vector machine classifiers. The diversity and cluster assumption criteria are defined by exploiting the properties of the self-organizing map neural networks. To assess the effectiveness of the proposed method, we compared it with several other active learning methods existing in the remote sensing literature by using both multi-spectral and hyperspectral remote sensing data sets. Experimental results confirmed the effectiveness of the proposed technique.