Semi-supervised cloud screening with Laplacian SVM
This work evaluates a new semi-supervised classification framework based on kernel methods and graph theory. In particular, the support vector machine (SVM) is further regularized with the un-normalized graph Laplacian, thus leading to the proposed Laplacian SVM. The method is tested in the challenging problem of cloud screening where the objective is to identify clouds in multispectral images acquired by space-borne sensors working in the visible and near-infrared spectral range. Preliminary results obtained using MERIS/ENVISAT data show the potential of the proposed Laplacian SVM in several scenarios.