Belief Function Classification with Conflict Management: Application on Forest Image

Treating imprecise and uncertain data requires an adequate formalism allowing a fit modelization. Several formalisms can be identified such as Bayesian theory, fuzzy set theory and belief function theory. The belief function theory provides an adequate formalism to manipulate those imperfect data. It also allows source fusion thanks to the combination operators that it integrates. The fusion process generates an empty set mass denoted conflict that illustrates the contradiction rate between considered sources. In this work, we tackle the classification of a forest high-resolution remote-sensing image problem. In order to classify this image, we handled imperfect information with the belief function theory. We propose a method for classification based on belief function theory and source fusion. The introduced Redistributing Conflict Classification Approach (RCCA) analyzes the conflict resulting from the fusion and redistributes it to the most pertinent classes. An experimental comparison to well known literature classifiers is provided.