Hyperspectral image classification using Gaussian process models

Hyperspectral image processing has been a very dynamic area in remote sensing and other applications since last decades. Hyperspectral images provide abundant spectral information to identify and distinguish spectrally similar materials. Recent advances in kernel machines promote the novel use of Gaussian processes (GP) for classifying hyper-spectral images. Many sophisticated kernel functions have been provided for kernel-based methods. However, different kernel functions has different performance in different applications. This paper introduces GP models with different kernel functions for classifying hyperspectral images. We first provided the mathematical formulation of GP models for classification. Then, several popular kernel functions and their hyperparaeters selection for GP models are introduced. The experiment are performed on three benchmark datasets to evaluate the performances of different kernel functions in terms of classification accuracy. Their performances are compared with each other and discussed in detailed.