Estimating Biophysical Parameters from Remotely Sensed Imagery with Gaussian Processes

Recently, a new machine learning approach that is based on the Gaussian process (GP) theory has been introduced in the literature. According to this approach, the learning of a machine (regressor or classifier) is formulated in terms of a Bayesian estimation problem, where the parameters of the machine are assumed to be random variables which follow jointly a Gaussian distribution. The purpose of this work is to investigate this approach in the context of the estimation of biophysical parameters. Experimental results obtained on synthetic and real data, which simulate the spectral behavior of the chlorophyll concentration in subsurface waters, are reported and compared with those yielded by the general regression neural network (GRNN) and the epsiv-insensitive support vector regression (SVR) methods.