Automated Pipeline Extraction from Interferometric Sar Data of the Ers Tandem Mission

A new method for the automated extraction of pipelines and roads from Synthetic Aperture Radar (SAR) scenes is presented. It combines intensity data with coherence data from an interferometric evaluation of a SAR scene pair. The fusion is based on Bayesian statistics and part of a Markov random field (MRF) model for line extraction. Both, intensity and coherence data are evaluated using rotating templates. The different statistical properties of intensity and coherence are taken into account by a multiplicative noise model and an additive noise model, respectively. The MRF model introduces prior knowledge about the continuity and the narrowness of lines. Posterior odds resulting from the MRF method are input to a ziplock snake-based method for linear object extraction. This processing step is controlled interactively which seems to be necessary as long as fully automatic processing of noisy data does not provide sufficiently predictable results. The method is applied to data of the ERS tandem mission.