Information fusion for supervised classification in a satellite image

In this paper, we present a multisource information-fusion method for satellite image classification. The main characteristics of this method are the use of possibility theory to handle the uncertainty connected with pixel classification, and the ability to mix numeric sources (the satellite image spectral bands) and symbolic sources (expert knowledge about best localisation of classes and out-image data for example). Moreover, this information fusion method is low time consuming and with a linear complexity. First we introduce briefly the possibility theory and the conjunctive fusion method used here. Then we apply this fusion method to a satellite image classification problem. The classes are defined by their spectral response on the one hand, and by the description of their best geographical context on the other hand. We compute the possibility distribution for the numeric sources on the one hand, and for the symbolic sources on the other hand. Finally the fusion handles the possibility measures coming from the numeric sources and from the symbolic sources