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A multiobjective PSO inflation methodology for SVM regression with limited training samples

In this paper, we present a novel multiobjective particle swarm optimization (MOPSO) approach for SVM regression with limited training samples. This approach, which is applied to the estimation of biophysical parameters from remote sensing images, is an extension of a work recently presented in the literature. It aims at exploiting unlabeled samples available from the image under analysis at zero cost to increase further the accuracy of the estimation process. The integration of such samples is made by optimizing simultaneously two criteria expressing the generalization capability of the SVM estimator, namely, the support vector count and the empirical risk. Experimental results obtained on synthetic and real multispectral data, which simulate the spectral behavior of the chlorophyll concentration in subsurface waters, are reported and discussed.