The Kohonen map for credal classification of large multispectral images

In the framework of the evidence theory, several approaches for estimating belief functions have been proposed. However, they generally suffer from the problem of masses attribution in case of compound hypotheses that lose much conceptual contribution of the theory. In this paper, an original method for estimating mass functions using the Kohonen map derived from the initial feature space and an initial classifier is proposed. Our approach allows a smart mass belief assignment, not only for simple hypotheses, but also for disjunctions and conjunctions of hypotheses. Thus, it can model at the same time ignorance, imprecision and paradox. The proposed method for basic belief assignment (BBA) is of interested for solving estimation mass functions problems where a large quantities of multi-variate data is available, such as remote sensing images. Indeed, the use of the Kohonen map simplify the process of assigning mass functions. In order to experimentally validate our work, we applied the proposed method to real data sets on image classification problem. Experimentation on SPOT images show that our approach gives better results than other methods presented in the literature.