Information combination operators for data fusion: a comparative review with classification

In most data fusion systems, the information extracted from each image or sensor (either numerical or symbolic) is represented as a degree of belief in an event with real values, generally in [0,1], taking in this way into account the imprecise, uncertain and incomplete nature of information. The combination of such degrees of belief is performed through numerical fusion operators. A very large variety of such operators has been proposed in the literature. We propose a classification of these operators issued from different data fusion theories (probabilities, fuzzy sets, possibilities, Dempster- Shafer, etc.) w.r.t. their behaviour. Three classes are thus defined: context independent constant behaviour operators, which have the same behaviour (compromise, disjunction or conjunction) whatever the values to be combined, context independent variable behaviour operators (whose behaviour depends on the combined values), and context dependent operators (where the result depends also on some global knowledge like conflict, reliability of sources). This classification provides a guide for choosing an operator in a given problem. This choice can then be refined from the desired properties of the operators, from their decisiveness, and by examining how they deal with conflictual situations. These aspects are illustrated on simple examples in multispectral satellite imaging. In particular, we stress the interest of the third class for classification problems.