Imperfect Multisource Spatial Data Fusion Based on a Local Consensual Dynamics

Abstract

Strategies for multisource spatial data fusion have generally to cope with distinct kinds of uncertainty, related to both the trust of the information source, the imperfection of spatial data, and the vagueness of the fusion strategy itself. In this chapter we propose a consensual fusion method that allows to flexibly model several fusion strategies ranging from a risk-taking to a risk-adverse attitude, and capable to cope with both data imprecision and source reliability. Uncertainty and imprecision in spatial data are represented by associating a fuzzy value with each spatial unit. The fusion function models a consensual dynamics and is parameterized so as to consider a varying spatial neighborhood of the data to fuse. Moreover the fusion has a quantifier-guided nature, reflecting the concept of a fuzzy majority and works on imprecise values to compute an imprecise result. It is formalized by a generalized OWA operator defined in the paper for aggregating imprecise values with distinct importance. The consensual fusion works so that the greater the trust score of the source and its agreement with the other sources, the more influent (important) is the data from the source in determining the consensual values. Thus the obtained fused map is determined in each location by a distinct majority of the sources, those that locally are in agreement. In cases where the data are affected by uncertainty one can require to fuse them so as to compute a result affected by at most a given maximum uncertainty level.