Band Grouping versus Band Clustering in SVM Ensemble Classification of Hyperspectral Imagery

Abstract

Due to the dense sampling of spectral signatures of land covers, hyperspectral images have a better discrimination among similar ground cover classes than traditional remote sensing data. However, these images are usually composed of tens or hundreds of spectrally close bands, which result in high redundancy and great amount of computation time in hyperspectral image classifi cation. In addition, the large number of spectral bands, but limited availability of training samples creates the problem of Hughes phenomenon. Consequently, traditional classifi cation strategies have often limited performance in classifi cation of hyperspectral imagery. Referring to the limitation of single classifi ers in these situations, classifi er ensemble system may exhibit better performance. This paper presents a method for classifi cation of hyperspectral data based on two concepts of Band Clustering (BC) and Band Grouping (BG) through a Support Vector machine (SVM) ensemble system. The proposed method uses the BC\BG strategies to split data into few band portions. After this step, we applied SVM on each band cluster\group that is produced in previous step. Finally, Naive Bayes as a classifi er fusion method combines the decisions of SVM classifi ers. Experimental results show that the proposed method improves the classification accuracy in comparison to the standard SVM and to feature selection methods.