Partially supervised oil-slick detection by SAR imagery using kernel expansion

Type Article
Date 2006-10
Language English
Author(s) Mercier Grégoire1, Ardhuin FannyORCID2, 3
Affiliation(s) 1 : Ecole Natl Super Telecommun Bretagne, ITI Dept, CNRS, UMR 2872,TAMCIC,TIME Team, F-29238 Brest, France.
2 : CNES, F-75001 Paris, France.
3 : IFREMER, DOPS, LOS, F-29280 Plouzane, France.
Source IEEE Transactions on Geoscience and Remote Sensing (0196-2892) (IEEE Geoscience and Remote Sensing Society), 2006-10 , Vol. 44 , N. 10 , P. 2839-2846
DOI 10.1109/TGRS.2006.881078
WOS© Times Cited 58
Keyword(s) Water pollution, Synthetic aperture radar, Sea surface, Satellite applications, Oil spill, Image analysis
Abstract 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.
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