Semisupervised Gaussian process regression for biophysical parameter estimation

In this paper, we propose a novel semisupervised Gaussian regression approach for the estimation of biophysical parameters from remote sensing data with limited training samples. During the learning phase, unlabeled samples are exploited to inflate the training set. The estimation of the targets associated with these samples is carried out by solving an optimization problem formulated within a genetic optimization framework. The search process of the target estimates is guided by the separate or joint optimization of two different criteria expressing the generalization capabilities of the GP estimator. The first is the empirical risk quantified in terms of the mean square error (MSE) measure; and the second is the log marginal likelihood. This last merges two terms expressing the model complexity and the data fit capability, respectively. Experimental results obtained on a real dataset representing chlorophyll concentrations in coastal waters confirm the interesting capabilities of the proposed approach.