Ideal Kernel-Based Multiple Kernel Learning for Spectral-Spatial Classification of Hyperspectral Image

Abstract

Using multiple types of features can effectively improve the classification accuracy of hyperspectral image (HSI). Multiple kernel learning (MKL) provides a flexible framework to fuse different features in a very natural way. In this letter, a novel MKL algorithm is proposed to integrate multiple types of features [i.e., principal components of original spectrum, multistructure morphological profiles (MPs), and multiattribute MPs] for HSI classification. The basic kernels are constructed with each feature subset separately, and the weights of basic kernels are determined by solving an optimization problem with the objective of the ideal kernel. Then, linear programming (LP) and signal sparse representation are adopted to solve the optimization problem, thus leading to two variants of the proposed algorithm, ideal kernel MKL (IKMKL)-LP and IKMKL-sparse, respectively. Experiments carried out on real hyperspectral data show that the proposed algorithms outperform several state-of-the-art MKL algorithms and reveal the capability of ranking the relevant features.