A Novel Active Learning Method for Support Vector Regression to Estimate Biophysical Parameters from Remotely Sensed Images

This paper presents a novel active learning (AL) technique in the context of ε-insensitive support vector regression (SVR) to estimate biophysical parameters from remotely sensed images. The proposed AL method aims at selecting the most informative and representative unlabeled samples which have maximum uncertainty, diversity and density assessed according to the SVR estimation rule. This is achieved on the basis of two consecutive steps that rely on the kernel k-means clustering. In the first step the most uncertain unlabeled samples are selected by removing the most certain ones from a pool of unlabeled samples. In SVR problems, the most uncertain samples are located outside or on the boundary of the ε-tube of SVR, as their target values have the lowest confidence to be correctly estimated. In order to select these samples, the kernel k-means clustering is applied to all unlabeled samples together with the training samples that are not SVs, i.e., those that are inside the ε-tube, (no...