Statistical Similarity Measure for Oil Slick Detection in SAR Image

Spaceborne Synthetic Aperture Radar (SAR) is well adapted to detect ocean pollution independently from daily or weather condition. As it is sensitive to surface roughness, the presence of oil film on the sea surface decreases the backscattering of the sea surface resulting in a dark feature patches in SAR images. In fact, oil slicks have specific impact on ocean wave spectra. Initial wave spectra may be characterized by three kinds of waves, big, medium and small, which correspond physically to gravity and gravity-capillary waves. The increase of viscosity due to the presence of oil damps gravity-capillary waves. This induces a damping of the backscattering to the sensor, but also a damping of the energy of the wave spectra, then it modifies the sea surface roughness observed by the sensor. Thus, local detection of wave spectra modification may be achieved by a appropriated texture analysis of the original SAR image. In this paper, the texture analysis is based on measure of similarity between a local probability density function (pdf) of clean water and the local pdf of the zone to be inspected. The local distribution is estimated in the neighbourhood of each pixel, through a sliding window, and compared to the reference one by using the Kullback-Leibler (KL) distance between distributions. An efficient strategy has been adopted in order to perform pdf estimation through a non-parametric approach.