Very recent articles report that Support Vector Machine (SVM) methods generally outperform traditional statistical and neural methods in classification problems involving hyperspectral images. In this paper we investigate the performance of the SVM classifier when applied to high dimensional image data, depicting natural scenes. Since the SVM classifier deals with a pair of classes at a time, a multi-stage classifier, structured as a binary tree is proposed, comparing classification approaches based on three different feature selection methods, i.e., selection of N features at regular intervals throughout the electromagnetic spectrum, Sequential Forward Selection (SFS) and the Recursive Feature Elimination technique (RFE). The RBF kernel is used in this study. Tests are performed using AVIRIS hyperspectral image data covering a test area which includes classes spectrally very similar, separable in high-dimensional spaces only.Pages: 7729-773
