Hyperspectral image classification using KNWFE with conformal transformation for kernel selection

Kernel nonparametric feature extraction (KNWFE) is a power tool for hyperspectral image classification in feature space. However, the performances of KNWFE largely depend on the choices of kernels. In this paper, a method is proposed for optimizing kernel function using the weighted means of KNWFE and the Fisher scalar with respect to KNWFE. The idea is to obtain a feature space with the largest nonparametric separability of training samples between classes by employing the parameters of a conformal transformation of a basic kernel. Experimental results show that the proposed method has a remarkable improvement of KNWFE for real data for two-classe problem.