A new approach for accurate classification of hyperspectral images using Virtual Sample Generation by Concurrent Self-Organizing Maps

This paper presents an original approach for improving performances of the supervised classifiers in hyperspectral remote sensing imagery using generation of synthetic samples to optimize the training set. The proposed model called Virtual Sample Generation by Concurrent Self-Organizing Maps (VSG-CSOM) is based on the idea of improving the training set of a supervised classifier by substituting the initial labeled sample set with the ''virtual” samples generated with a system of concurrent SOMs. We have considered a Spatial-Contextual Support Vector Machine (SC-SVM) classifier, taking into consideration both intraband and also interband pixel correlation. The proposed method is implemented and evaluated using two of the most known data sets of hyperspectral images: Indian Pines and Pavia University. The experimental results prove the significant advantage of the new model.