A multi-scale Markov model for unsupervised oil spill detection in TerraSAR-X data

Abstract

In this paper an automatic near-real time oil spill detection approach using single-polarized high resolution X-Band Synthetic Aperture Radar satellite data is presented. Dark formations on the water surface are classified in a completely unsupervised way using an automatic tile-based thresholding procedure. The derived global threshold value is used for the initialization of a hybrid multi-contextual Markov image model which integrates scale-dependent and spatial contextual information on irregular hierarchical graph structures into the segment-based labeling process of slick-covered and slick-free water surfaces. Experimental investigations performed on TerraSAR-X ScanSAR data acquired during large-scale oil pollutions in the Gulf of Mexico in May 2010 confirm the effectiveness of the proposed method with respect to accuracy and computational effort.