The purpose of this paper is to define the mass functions for a set of multiple mixed hypothesis in an context of Dempster/Shafer (DS) theory which offers an interesting tool to combine data providing from heterogeneous sources more or less reliable by managing imprecision and uncertainty. This is particularly important when dealing with multi-modality imaging (satellite image), where the fusion of information increases the global knowledge about the phenomenon while decreasing the imprecision and uncertainty about it. This theory also enables us to assign masses to 2D elements (D: decision space) rather than to D elements as in probabilistic theory. The DS has been used in many applications in the field of image analysis, but without its all powerful. When using with only simple hypothesis (an object belongs to only one class), the theory falls in the probabilistic case, which is considered as a particular case. Bloch and Barnett attempt to use double hypothesis but their method still remains particular and restrictive. We propose in this paper a method to extract for a class the consonance and dissonance degrees among several classifiers (methods), and the integration of these terms to initialize the mass functions with multiple mixed hypothesis in order to use the orthogonal Dempster/Shafer Rule. The problem must be viewed from multiclass, multi-sources (images) and multi- point of view (methods or classifiers used) context. We first show how our method works with 1 -- image, 2 -- classifiers, and 2 -- hypothesis and then generalize for P - - images, K -- sources and 2D -- hypothesis.