Semi-supervised PSO-SVM regression for biophysical parameter estimation

In this paper, a novel semisupervised regression approach is proposed to tackle the problem of biophysical parameter estimation that is constrained by a limited availability of training (labeled) samples. The main objective of this approach is to increase the accuracy of the estimation process based on the support vector machine (SVM) technique by exploiting unlabeled samples that are available from the image under analysis at zero cost. The integration of such samples in the regression process is controlled through a particle swarm optimization (PSO) framework that is defined by considering separately or jointly two different optimization criteria, thus leading to the implementation of three different inflation strategies. These two criteria are empirical and structural expressions of the generalization capability of the resulting semisupervised PSO-SVM regression system. The conducted experiments were focused on the problem of estimating chlorophyll concentrations in coastal waters from multispectral remote sensing images. In particular, we report and discuss results of experiments that are designed in such a way as to test the proposed approach in terms of: 1) capability to capture useful information from a set of unlabeled samples for improving the estimation accuracy; 2) sensitivity to the number of exploited unlabeled samples; and 3) sensitivity to the number of labeled samples used for supervising the inflation process.