Robust multiple estimator systems for the analysis of biophysical parameters from remotely sensed data

An approach based on multiple estimator systems (MESs) for the estimation of biophysical parameters from remotely sensed data is proposed. The rationale behind the proposed approach is to exploit the peculiarities of an ensemble of different estimators in order to improve the robustness (and in some cases the accuracy) of the estimation process. The proposed MESs can be implemented in two conceptually different ways. One extends the use of an approach previously proposed in the regression literature to the estimation of biophysical parameters from remote sensing data. This approach integrates the estimates obtained from the different regression algorithms making up the ensemble by a direct linear combination (combination-based approach). The other consists of a novel approach that provides as output the estimate obtained by the regression algorithm (included in the ensemble) characterized by the highest expected accuracy in the region of the feature space associated with the considered pattern (selection-based approach). This estimator is identified based on a proper partition of the feature space. The effectiveness of the proposed approach has been assessed on the problem of estimating water quality parameters from multispectral remote sensing data. In particular, the presented MES-based approach has been evaluated by considering different operational conditions where the single estimators included in the ensemble are: 1) based on the same or on different regression methods; 2) characterized by different tradeoffs between correlated errors and accuracy of the estimates; 3) trained on samples affected or not by measurement errors. In the definition of the ensemble particular attention is devoted to support vector machines (SVMs), which are a promising approach to the solution of regression problems. In particular, a detailed experimental analysis on the effectiveness of SVMs for solving the considered estimation problem is presented. The experimental results point out that the SVM method is effective and that the proposed MES approach is capable of increasing both the robustness and accuracy of the estimation process.