Automated identification of man-made textural features on satellite imagery by Bayesian networks

A classification technique which distinguishes between man-made and natural textural features visible on high resolution satellite images is introduced. The proposed work aims to evaluate non-linear classification techniques by the unification of appropriate texture analysis methods and a learning Bayesian classifier which is more robust against data uncertainty than the other types of linear classifiers. The classification technique introduced within this work will also provide an opportunity for fully automated thematic and land-use map generation.