In this paper we investigate an optimization procedure using the binary classifier Support Vector Machines (SVM) applied to highdimensional image data (hyperspectral image data) in a multiclass problem. In this particular case, one problem that has been investigated refers to the optimal choice for the parameters in the selected kernel function. Different approaches have been proposed in the literature for global optimization of the kernel parameters in a multiclass problem. In this study we investigate the use of a binary tree with the kernel parameters estimated at every tree node, by using the global accuracy as an optimization criterion. The proposed methodology is tested by using hyperspectral image data collected over the Indian Pine test area. The global classification accuracy yielded by the proposed methodology is compared with the results of a similar procedure implementing no optimization procedure.Pages: 2298-230
