Robust Estimation of Water Chlorophyll Concentrations With Gaussian Process Regression and IOWA Aggregation Operators

In this paper, we propose a new framework for estimating water chlorophyll concentrations in remote sensing data based on Gaussian process regression (GPR) and induced ordered weighted averaging(IOWA) operators. First, we construct an ensemble of GPR estimators modeled with different covariance functions. Then, in a second step, we aggregate the predictions of these estimators using IOWA operators. To learn the weights associated with these nonlinear operators, we propose three different approaches called IOWA(MVO), IOWA(MOP), and IOWA(PA). The IOWA(MVO) is based on the minimization of the variance of the weights with a given orness level. In IOWA(MOP), we replace the orness level constraint by an objective related to data fitting. Then we solve the modified optimization problem using a multiobjective optimization evolutionary algorithm based on decomposition. Finally, in IOWA(PA), we generate the weights directly from the confidence measures (i.e., output variances) provided by the set of GPR estimators using the concept of prioritization aggregation. Experimental results on in situ and satellite data are reported and discussed.