Applying automatic kernel parameter selection method to the full bandwidth RBF kernel function for hyperspectral image classification

The support vector machine (SVM) is widely used in hyperspectral image classification due to the robust to the Hughes phenomenon. However, the performance of SVM highly depends on the kernel parameter selection. Hence, it is hard to apply the SVM based on the kernel with lots of parameters such as the full bandwidth RBF (FRBF) kernel whose number of parameters is equal to the number of features. In our previous study, an automatic kernel parameter selection method (APS) was proposed for the normalized kernel function. The proper kernel parameters are the minimizer of the optimization problem based on the proposed kernel-based class separability measure. In this study, we apply the APS to find the best kernel parameters of the FRBF kernel. Experimental results on the Indian Pine Site dataset show that the SVM based on the FRBF kernel with proper kernel parameters outperforms than the SVM based on the RBF kernel on the small sample size problem.