Generalized optimal Kernel-Based Ensemble Learning for hyperspectral classification problems

In this paper, a Generalized Kernel-based Ensemble Learning (GKEL) algorithm for hyperspectral classification problems is presented. The proposed algorithm generalizes the Sparse Kernel-based Ensemble Learning (SKEL) technique, developed previously by the authors. SKEL optimally and sparsely weights and aggregates an ensemble of individual SVM classifiers which independently conduct learning within their corresponding randomly selected spectral feature sub-space using a Gaussian kernel. This ensemble decision is fully optimal, if the dimensionality of the randomly selected feature subspaces and the initial number of the sub-classifiers are determined optimally and is sub-optimal, otherwise. This sub optimality issue is addressed by taking a bottom-up approach. Individual sub-classifiers are added one-by-one optimally to the ensemble until the ensemble converges. The feature sub space of each individual classifier is optimally selected. The ensemble is modeled as a Quadratically-Constrained Linear Programming (QCLP) problem and optimized by combining Multiple Kernel Learning (MKL) with a greedy, non-linear integer programming method for non-monotonic sparse feature sub-space selection. Hyperspectral image data as well as multivariate data are used to verify the performance improvement of the proposed GKEL algorithm over SKEL in detecting difficult targets.