Improvement of Land Cover Map from Satellite Imagery using DST and DSmT

The aim of this paper is to show that Dempster-Shafer theory (DST) and a recent theory of plausible and paradoxical reasoning introduced by Dezert and Smaradache and thus called Dezert-Smarandache theory (DSmT), can be successfully applied to improve a supervised classification of remotely sensed data. Notice that application fields of these two theories are related on multisensor/multitemporal/multiscale data fusion. In this study, our contribution lies in developing a new multispectral data classification process which can be seen as a multisensor fusion process where each thematic class is considered as one source of information