Active fusion - A new method applied to remote sensing image interpretation

Axel Pinz, Manfred Prantl, Harald Ganster, Hermann Borotschnig

Abstract: Today's computer vision applications often have to deal with multiple, uncertain and incomplete visual information. In this paper, we introduce a new method, termed “active fusion”, which provides a common framework for active selection and combination of information from multiple sources in order to arrive at a reliable result at reasonable costs. The implementation of active fusion on the basis of probability theory, the Dempster-Shafer theory of evidence and fuzzy sets is discussed. In a sample experiment, active fusion using Bayesian networks is applied to agricultural field classification from multitemporal Landsat imagery. This experiment shows a significant reduction of the number of information sources required for a reliable decision.