Ideal Regularized Composite Kernel for Hyperspectral Image Classification

Abstract

This paper proposes an ideal regularized composite kernel (IRCK) framework for hyperspectral image (HSI) classification. In learning a composite kernel, IRCK exploits spectral information, spatial information, and label information simultaneously. It incorporates the labels into standard spectral and spatial kernels by means of the ideal kernel according to a regularization kernel learning framework, which captures both the sample similarity and label similarity and makes the resulting kernel more appropriate for specific HSI classification tasks. With the ideal regularization, the kernel learning problem has a simple analytical solution and is very easy to implement. The ideal regularization can be used to improve and to refine state-of-the-art kernels, including spectral kernels, spatial kernels, and spectral-spatial composite kernels. The effectiveness of the proposed IRCK is validated on three benchmark hyperspectral datasets. Experimental results show the superiority of our IRCK method over the classical kernel methods and state-of-the-art HSI classification methods.