A kernel based nonlinear subspace projection method for reduction of hyperspectral image dimensionality

In this paper, a kernel-based nonlinear subspace projection (KNSP) method is proposed for reduction of hyperspectral image dimensionality. This method involves three steps: subspace partition of full data space, feature extraction based on kernel principal component analysis (KPCA) in subspace and feature selection based on class separability criterion. The main merit of the proposed method is that it is more suitable for feature extraction than linear principal component analysis (PCA) and segmented principal component transformation (SPCT), in particular, when hyperspectral data have nonlinear characteristics. In order to testify the effectiveness of KNSP method for reduction of hyperspectral image dimensionality, hyperspectral image classification is performed on AVIRIS data. Experimental results show that when the hyperspectral dimensionality is reduced to a few features, average classification accuracy of the new method is higher than those of PCA and SPCT methods.