A novel non-parametric weighted feature extraction method for classification of hyperspectral image with limited training samples

Abstract

In this paper, a novel non-parametric weighted linear feature extraction method has been developed for classifying hyperspectral image data with limited training samples. Within this framework, we found two important vectors for each training sample and calculated the magnitude of projection of the two vectors to weight it when designing the within-class and between-class scatter matrix. The effectiveness of the proposed feature extraction scheme as compared to two other non- parametric feature extraction methods, nonparametric weighted feature extraction (NWFE), and nonparametric discriminant analysis (NDA), is demonstrated using Washington DC Mall data. From the experimental results, the proposed method is remarkably powerful and robust.