Microwave Remote sensing sea surfaces covered in oil

This paper presents the recent research and progress of monitoring sea surfaces covered in oil using microwave remote sensing technology. The influence of the height spectrum of a contaminated sea, by comparison with a clean sea, on the scattering coefficient has been studied. It was found that the oil slick damps the capillarity waves of the surface spectrum, implying a lower surface rms slope. A fully automated system for the identification of possible oil spills present on Synthetic Aperture Radar (SAR) satellite images based on artificial intelligence fuzzy logic has been developed. The system analyzes the satellite images and assigns the probability of a dark image shape to be an oil spill. The output consists of several images and tables providing the user with all relevant information for decision-making. A fully plarimetric electromagnetic model for sea surface Mueller matrix is exploited to characterize the scattering from oil and biogenic slicks, under low-to-moderate wind conditions. A simple and very effective filtering technique is proposed for SAR sea oil slick observation. Experiments, accomplished over C-band multilook complex SIR-CIXS AR data, show the effectivenes of the proposed model and the capabilities of the filter to both observe oil slicks and distinguish them from biogenic look-alikes. Finally the technical challenges of microwave wave Remote sensing sea surfaces covered in oil are discussed.