In this paper, we propose an unsupervised classification scheme based on the Dempster-Shafer Theory (DST) and the Dezert-Smarandache Theory (DSmT) to characterize vegetated, aquatic and mineral surfaces. From pre-processed ASTER satellite images (georeferencing, geometric correction and 15 m re-sampling), neo-channels were produced by determining the spectral indices NDVI, MNDWI and NDBaI, considered as sources of information for classification of a given pixel. Then, we modeled respectively the formalisms of the DST and the DSmT and we realized the algorithms and related codes that we implemented in the MATLAB environment. Our contribution lies in taking into account the imperfections (inaccuracies and uncertainties) linked to source information through the use of mass functions based on a simple Gaussian distribution support model in order to model each focal element independently of the others and to evaluate the belonging of a pixel to a class with respect to the majority of elements representing said class. The resulting results show that the DST approach is relatively satisfactory for the unsupervised classification of mineral surfaces and aquatic surfaces while it is not satisfactory for vegetated surfaces according to all proposed models. As for the DSmT, it presents satisfactory results for all the models proposed. The model with the exclusion integrity constraint E (Intersection) V (Intersection) M= Phi was selected as the best model because having, in addition to an average rate of well-graded pixels of 93.34%, a compliance rate of 96, 37% with the terrain higher than those of the other models implemented.

This study aims to evaluate the hydraulic properties of crystalline and crystallophyllian bedrock aquifers of N'zi-Como