Oil slick detection by SAR imagery using Support Vector Machines

Abstract

Spaceborne Synthetic Aperture Radar (SAR) is well adapted to detect ocean pollution independently from daily or weather condition. 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 is 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. Thus, local segmentation of wave spectra may help oil slick detection. It can be achieved by the segmentation of a multiscale decomposition of the original SAR image. In this work, a supervised oil slick detection is proposed by using Support Vector Machines into the wavelet decomposition of a SAR image. It performs accurate detection with no consideration to signal stationarity nor to the presence of strong backscatters (such as ship). Moreover, when using normalized SAR images, the kernel expansion may be generalized from one image to an other to make a near unsupervised detection scheme. The algorithm has been applied on Envisat ASAR images. First experiments yield accurate segmentation results with a very limited number of false alarms.