Classification of Hyperspectral Remote Sensing Images Using Gaussian Processes

In this paper, we explore the effectiveness of the Bayesian Gaussian process approach for classifying hyperspectral remote sensing images. In particular, we consider two analytical approximation methods for Gaussian process classification, which are the Laplace and the expectation propagation methods. Experimental results obtained on a benchmark hyperspectral dataset show that, in terms of classification accuracy, Gaussian process classification can compete seriously with the state-of-the-art classification approach based on support vector machines.