TY - JOUR T1 - Partially supervised oil-slick detection by SAR imagery using kernel expansion A1 - Mercier,Grégoire A1 - Ardhuin,Fanny AD - Ecole Natl Super Telecommun Bretagne, ITI Dept, CNRS, UMR 2872,TAMCIC,TIME Team, F-29238 Brest, France. AD - CNES, F-75001 Paris, France. AD - IFREMER, DOPS, LOS, F-29280 Plouzane, France. UR - https://archimer.ifremer.fr/doc/00000/1948/ DO - 10.1109/TGRS.2006.881078 KW - Water pollution KW - Synthetic aperture radar KW - Sea surface KW - Satellite applications KW - Oil spill KW - Image analysis N2 - Spaceborne synthetic aperture radar (SAR) is well adapted to detect ocean pollution independently from daily or weather conditions. In fact, oil slicks have a specific impact on ocean wave spectra. Initial wave spectra may be characterized by three kinds of waves, namely 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 not only 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 be achieved by the segmentation of a multiscale decomposition of the original SAR image. In this paper, a semisupervised oil-slick detection is proposed by using a kernel-based abnormal detection 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 a ship). The algorithm has been applied on ENVISAT Advanced SAR images. It yields accurate segmentation results even for small slicks, with a very limited number of false alarms. Y1 - 2006/10 PB - IEEE Geoscience and Remote Sensing Society JF - IEEE Transactions on Geoscience and Remote Sensing SN - 0196-2892 VL - 44 IS - 10 SP - 2839 EP - 2846 ID - 1948 ER -