Hyperspectral Image Classification Using Kernel-based Nonparametric Weighted Feature Extraction

Abstract

Usually feature extraction is applied for dimension reduction in hyperspectral data classification problems. Some studies show that nonparametric weighted feature extraction (NWFE; Kuo and Landgrebe, 2004) is a powerful tool to extract hyperspectral image features for classification. Recently, some studies also show that kernel-based methods are computationally efficient, robust and stable for pattern analysis. In this study, a kernel-based NWFE (KNWFE) is proposed for hyperspectral image classification. In this paper, we show that KNWFE is a generalization of original NWFE.