Active fusion using Bayesian networks applied to multi-temporal remote sensing imagery

Image processing applications and especially those in the area of remote sensing are often characterized by a high degree of complexity. We introduce a general framework, called `active fusion´, that actively selects and combines information from multiple sources in order to obtain a reliable result at reasonable costs. A sample implementation of parts of the framework is given using Bayesian networks and decision theoretic techniques for the task of agricultural field classification. This experiment shows a significant reduction in the number of information sources required for a reliable decision