A novel covariance function for predicting vegetation biochemistry from hyperspectral imagery with Gaussian processes

Remotely extracting information about the biochemical properties of the materials in an environment from airborne- or satellite-based hyperspectral sensor has a variety of applications in forestry, agriculture, mining, environmental monitoring and space exploration. In this paper, we propose a new non-stationary covariance function, called exponential spectral angle mapper (ESAM) for predicting the biochemistry of vegetation from hyperspectral imagery using Gaussian processes. The proposed covariance function is based on the angle between the spectra, which is known to be a better measure of similarity for hyperspectral data due to its robustness to illumination variations. We demonstrate the efficacy of the proposed method with experiments on a real-world hy-perspectral dataset.