Hyperspectral Image Classification With Kernel-Based Least-Squares Support Vector Machines in Sum Space

Hyperspectral image classification has attracted considerable interest in recent years. The previous classification methods are usually based on single-kernel or composite-kernel machines. In this paper, a novel regularization framework referred to least-squares support vector machine in sum space (LS-SVM-SS) of reproducing kernel Hilbert space (RKHS) is proposed for the classification of hyperspectral images using spectral signatures or local binary pattern features. The method is designed to simultaneously approximate the low- and high-frequency components of the target classification function with multiscale kernels. In the newly proposed scheme, LS-SVM-SS carries out the supervised learning and train a closed-form discriminant function to directly implement the multiclass classification. In contrast to multiple-kernel learning (MKL) by one-step method or two-step method, we can obtain a noniterative optimization procedure. Experiments are conducted on three real hyperspectral datasets. The corresponding experimental results demonstrate that the LS-SVM-SS method achieves good generalization performance in most cases compared with several state-of-the-art methods, which is better than that in any single-kernel RKHS. In addition, the proposed framework of multiscale kernels classifier opens a wide field for future developments in hyperspectral image classification.