A kernel-based feature extraction method for hyperspectral image classification

Most studies showed that most hyperspectral image classification encountered the Hughes phenomenon due to the redundant features, especially in the small sample size problem. Feature extraction method such as linear discriminant analysis (LDA), nonparametric weighted feature extraction (NWFE) is a preprocessing step before classification and used to combine and reduce the original features into a new feature space based on the between-class and with-class separability. Then, the classifier such as the nonlinear support vector machine (SVM) is trained and classifies the unknown samples. However, the separability measurement of LDA and NWFE is for the original space not the kernel-induced feature space. In this study, a kernel-based feature extraction method is proposed. The corresponding transformation matrix for dimension reduction is based on the class separability in the kernel-induced feature space which was proposed in our previous study. Experimental results on the Indian Pine Site dataset show that the proposed method improves the classification performance of the SVM on the small sample size problem.