Automatic detection and tracking of oil spills in SAR imagery with level set segmentation

Automatic detection and monitoring of oil spills and illegal oil discharges is of fundamental importance in ensuring compliance with marine legislation and protection of the coastal environments, which are under considerable threat from intentional or accidental oil spills, uncontrolled sewage and wastewater discharged. In this paper, the level set based image segmentation was evaluated for the real-time detection and tracking of oil spills from SAR imagery. The processing scheme developed consists of a pre-processing step, in which an advanced image simplification takes place, followed by a geometric level set segmentation for the detection of possible oil spills. Finally, a classification was performed for the separation of look-alikes, leading to oil spill extraction. Experimental results demonstrate that the level set segmentation is a robust tool for the detection of possible oil spills, copes well with abrupt shape deformations and splits and outperforms earlier efforts that were based on different types of thresholds or edge detection techniques. The developed algorithm's efficiency for real-time oil spill detection and monitoring was also tested.