The application and potential of Bayesian network fusion for automatic cartographic mapping

The research into automatic cartographic mapping is a current topic due to today´s availability of high resolution remote sensing data. In order to get as much reliable information as possible, it is recommendable to fuse different image data of the same scene. No matter if the images are acquired by different sensors, from different directions (i.e. multi-aspect data), or are multi-temporal, a careful fusion is required. In this paper we present a high-level decision fusion based on Bayesian network theory developed for automatic road extraction from multi-aspect SAR data. First, the Bayesian network theory is briefly introduced, followed by the process of developing the fusion for the road extraction: 1) Formulating the problem by means of a Bayesian network 2) Learning by estimating up conditional probabilities. Results of the fusion tested on TerraSAR-X data are presented. In the end the potential of the Bayesian network fusion for automatic mapping of cartographic features are discussed.