Partially supervised oil-slick detection by SAR imagery using kernel expansion
| Type | Article | ||||||||
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| Date | 2006-10 | ||||||||
| Language | English | ||||||||
| Author(s) | Mercier Grégoire1, Ardhuin Fanny 2, 3 |
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| 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. |
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| 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 | 62 | ||||||||
| 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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