Gabor feature‐based composite kernel method for hyperspectral image classification

Abstract

Different from the traditional kernel classifiers that map the original data into high-dimensional kernel space, a novel classifier that projects Gabor features of the hyperspectral image into the kernel induced space through composite kernel technique is presented. The proposed method can not only improve the flexibility of the exploitation of spatial information but also successfully apply the kernel technique from a very different perspective to strengthen the discriminative ability. Experiments on the Indian Pines dataset demonstrate the superiority of the proposed method.