A nonlinear feature selection method based on kernel separability measure for hyperspectral image classification

Abstract

Many research shows that we will encounter the Highes phenomenon when dealing with the high-dimensional data classification problem. In addition, non-linear support vector machine (SVM) has been shown that it can conquer the problem efficiently. However, the SVM is a black-box model based on the whole features and does not provide the feature importance or “good” feature subset for classification and other applications. In 2012, an automatic kernel parameter selection (APS) based on kernel-based within- and between-class separability measures were proposed. Moreover, the application for determining the kernel parameters of the full bandwidth RBF (FRBF) kernel was proposed. In this study, the bandwidths of the FRBF kernel were considered as the weights of the features when the feature values are rescaled by computing the z-scores. Experimental results on the Indian Pine Site dataset showed that the SVM based on the proposed feature subset outperforms than the SVMs based on the RBF kernel and FRBF kernel.