Kernel-based joint spectral and spatial exploitation using Hilbert space embedding for hyperspectral classification

摘要

In this paper, a Support Vector Machine (SVM) based method to jointly exploit spectral and spatial information from hyperspectral images to improve classication performance is presented. In order to optimally exploit this joint information, we propose to use a novel idea of embedding a local distribution of input hyperspectral data into the Reproducing Kernel Hilbert Spaces (RKHS). A Hilbert Space Embedding called mean map is utilized to map a group of neighboring pixels of a hyperspectral image into the RKHS and then, calculate the empirical mean of the mapped points in the RKHS. SVM based classication performed on the mean mapped points can fully exploit the spectral information as well as ensure spatial continuity among neighboring pixels. The proposed technique showed signicant improvement over the existing composite kernels on two hyperspectral image data sets.