A Generalized Appriou's Model for Evidential Classification of Multispectral Images: A Case Study of Algiers City

In this paper, we shall describe an evidential supervised classifier of multispectral satellite images. The evidence theory of Dempster-Shafer (DST) is used to take into account the ignorance and the uncertainty related to data, and so, overcome the Bayesian classifier limits. Notice that application fields of DST are initially related on multisensor, multitemporal and multiscale data fusion. In this study, our contribution lies in developing an evidential classification process that can be seen as a multisource fusion process where each predefined thematic class is considered as one source of information. The evidential mass functions of the considered thematic hypotheses are estimated using Appriou's transfer model whose we propose to generalize to a multi-class case. Developed DST-classifier has been tested on multispectral ETM+ image covering the urban north-eastern part of Algiers (Algeria). The spectral validation of obtained evidential classes allows us to confirm the accuracy of the resulting land cover map.