Automatic identification of oil spills on satellite images

Authors:
Iphigenia Keramitsoglou;Constantinos Cartalis;Chris T. Kiranoudis
Affiliations:
Remote Sensing and Image Processing Team, Department of Applied Physics, University of Athens, Panepistimioupolis, Build PHYS-V, Athens GR-15784, Greece;Remote Sensing and Image Processing Team, Department of Applied Physics, University of Athens, Panepistimioupolis, Build PHYS-V, Athens GR-15784, Greece;Department of Process Analysis and Systems Design, School of Chemical Engineering, National Technical University, Zografou Campus, Athens GR-15780, Greece
Venue:
Environmental Modelling & Software
Year:
2006

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Abstract

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. Oil spills are recognized by experts as dark patterns of characteristic shape, in particular context. 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. The case study area was the Aegean Sea in Greece. The system responded very satisfactorily for all 35 images processed. The complete algorithmic procedure was coded in MS Visual C++ 6.0 in a stand-alone dynamic link library (dll) to be linked with any sort of application under any variant of MS Windows operating system.