Bayesian Learning with Gaussian Processes for Supervised Classification of Hyperspectral Data

Abstract

Recent advances in kernel machines promote the novel use of Gaussian processes (GP) for Bayesian learning. Our purpose is to introduce GP models into the remote sensing community for supervised learning as exemplified in this study for classifying hyperspectral images. We first provided the mathematical formulation of GP models concerning both regression and classification; described several GP classifiers (GPCLs) and the automatic learning of kernel parameters; and then, examined the effectiveness of GPCLs compared with K-nearest neighbor (KNN) and Support Vector Machines (SVM). Experiment results on an Airborne Visible/Infrared Imaging Spectroradiometer image indicate that the GPCLs outperform KNN and yield classification accuracies comparable to or even better than SVMs. This study shows that GP models, though with a larger computation scaling than SVM, bring a competitive tool for remote sensing applications related to classification or possibly regression, particularly with small or moderate sizes of training datasets.