Unsupervised multiscale oil slick segmentation from SAR images using a vector HMC model

This study focuses on the segmentation and characterization of oil slicks on the sea surface from synthetic aperture radar (SAR) observations. In fact, an increase in viscosity due to oil notably reduces the roughness of the sea surface which plays a major part in the electromagnetic backscattering. So, an oil spill is characterized by low-backscattered energy and appears as a dark patch in a SAR image. This is the reason why most detection algorithms are based on histogram thresholding, but they do not appear to be satisfactory since the number of false alarms is generally high. By considering that a film has a specific impact on the ocean wave spectrum and by taking into account the specificity of SAR images, a vector hidden Markov chain (HMC) model adapted to a multiscale description of the original image is developed. It yields an unsupervised segmentation method that takes into account the different states of the sea surface through its wave spectrum. Thanks to mixture estimation, it is possible to characterize the detected areas and thus avoid most false alarms. Results of segmentation are shown in two types of scenarios. The first one concerns an oil spill in the Mediterranean sea detected by the ERS SAR sensor at a resolution of 25m. The second scenario is related to the wreck of the Prestige acquired by the Envisat ASAR sensor in a wide swath mode at a resolution of 150m.

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