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SUPERVISED PATTERN CLASSIFICATION TECHNIQUES FOR OIL SPILL CLASSIFICATION IN SAR IMAGES: PRELIMINARY RESULTS

ABSTRACT In this study, classical features extracted from Synthetic Aperture Radar (SAR) Images and used in oil spill classification procedures have been examined/evaluated and ranked in function of their effectiveness. The best features have been used to perform the classification of dark patches by means of two-class and one-class approaches. A number of one-class and two-class classification techniques have been considered: Linear discriminants, 1-Nearest Neighbour, Mixture of Gaussians, Parzen Windows and the Support Vector Machines. Better results have been achieved using the one-class methods. The dataset has been extracted from a set of SAR images acquired by an airborne X band SAR system mounted on board of a Laerjet 35A, during the Galitia Mission in January-February 2003 (TELAER Consortium), after the sinking of the Prestige oil tanker. 1 INTRODUCTION Oil spill detection by means of SAR images is possible because of the damping effect of the short wind waves caused by the presence of oil on the sea surface [1-2]. As a consequence, an oil spill is physically a dark patch in SAR images [1-2]. The sea radar image is a representation of the backscatter return, and the intensity of the pixel is proportional to the surface roughness at the scale of radar wavelength (Bragg scattering) [3]. The radar backscatter coefficient is a function of the viewing geometry of the SAR [3]. The b with the increase of the ackscatter coefficient decreasesincidence angle, and the scattering properties of a material depend on the polarization of the incoming radar signal [3]. The contrast between an oil spill and its surroundings depends on a number of parameters as the height of the waves, the amount of oil that has been released, the speed of the wind, etc [1-2]. The shape of the oil spill depends on a number of factors as whether the oil was released from a stationary object or from a moving ship, the amount of oil involved, the speed of the wind, and the current history in the time interval between the oil release and the acquisition of the image [4]. Unfortunately, several natural and atmospheric phenomena produce dark areas in SAR images similar to oil spills. These dark areas are usually referred to as look-alikes, whose presence makes the detection of oil spills a challenging task [1-2]. Oil spills may include all oil-related surface films caused by oil spills from oilrigs, leaking pipelines, passing vessels, while look-alikes include natural films, grease ice, threshold wind speed areas (<3 m/s), rain cells, etc. Oil spills are only man-made slicks due to crude petroleum and its products [1-2]. It is easy to see that oil spill detection over SAR images not only requires the detection of dark patches in the image, but also requires post-processing techniques aimed at discriminating oil spills from look-alikes. Oil spill detection is thus usually framed into three fundamental phases: