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Partially supervised classification of multisource data

A novel data fusion approach to partially supervised classification problems is presented, which allows a specific land-cover class of interest to be mapped by using only training samples belonging to such class. This represents a significant operational advantage in many application domains where end-users require information products for the monitoring of a specific or few land cover classes (e.g., forestry, urban monitoring) of interest. The proposed technique overcomes one of the main methodological drawbacks of this type of problems: i.e., the lack of prior knowledge on the statistics of the unknown classes present in the scene under consideration. Experiments carried out on a multisource data set demonstrate the validity of the proposed technique.