A data-fusion approach to partially supervised classification

Considers the problem of partially supervised classification under a data-fusion perspective. The objective is to map one class (or only few classes) of interest in multisensor remote-sensing data by using exclusively training samples belonging to such class (or classes). The proposed methodology is based on a combined use of a radial basis function (RBF)-like network and a Markov random field (MRF) approach.