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Oil slick detection by SAR imagery : application on Prestige accident images PowerPoint Presentation
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Oil slick detection by SAR imagery : application on Prestige accident images

Oil slick detection by SAR imagery : application on Prestige accident images

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Oil slick detection by SAR imagery : application on Prestige accident images

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  1. Oil slick detection by SAR imagery : application on Prestige accident images Fanny GIRARD-ARDHUIN1,2, F. COLLARD1, G. MERCIER2 et R. GARELLO2 1 BOOST technologies, Brest 2 GET-ENST Bretagne, TAMCIC 2658 CNRS, Brest

  2. Introduction Image analysis Measurement Application Detection Sensors Case study Slick detection and classification Classification Parameters Methodology Conclusion Outline

  3. accidents : 5% of total pollution Oil slicks Detect illegal discharges Evaluate slick drift Protect coasts Natural slicks intense biological activity Biologists Fishermen Natural/oil slicks reduce gaz exchanges Global climate change models Introduction Introduction

  4. Which platform ? Airborne survey © ESA Boat Satellite ENVISAT Which sensor ? Optic/visible Infrared Ultraviolet Lidar SAR : Synthetic Aperture Radar Radar sensitive to oil pollution make images through clouds SAR advantages does not depend on sunshine Measurement Measurement

  5. A radar measures the cross-section related to the roughness of the ocean surface at the scale of the wavelength slick damping impact on short waves roughness cross-section Slick effect and measurement experiences JONSWAP75,MARSEN79, SAXON FPN90, SAMPLEX92 Measurement

  6. slick dark patch Masuko et al., 1995 C, X and Ku-bands Wismann, 1993 Wavelength Measurement

  7. Wismann, 1993 high elasticity oil slick easier to detect thick Slick nature Measurement

  8. both oil and natural slicks detected 2<|V|<5 m/s generation of natural slick impossible 5<|V|<10-14 m/s if detection pollution turbulence and waves |V|>10-14 m/s which drags slicks in the ocean sub-surface 2<|V|<10-14 m/s no detection Meteorological and oceanic conditions Measurement

  9. locate outlines of dark areas in the image detection mathematical morphology filters original approach based on ocean surface characterization Mercier et al., 2003 determine if natural slick or pollution classification synergy with meteo-oceanic data : wind, surface currents, sea temperature, chlorophyll, waves… statistics about shape, length, size, wind history… Solberg et al., 1999 Espedal et al., 1999 neuronal network, fuzzy logic, etc… Some approaches Image analysis

  10. exceptional pollution : - huge quantity - drift over large distance ENVISAT and ERS images many acquisitions Daily aircraft tracking maps of detected slicks Case study “Prestige” tanker Application

  11. ENVISAT @ESA 38 x 38 km November 17, 2002 tanker Application : detection

  12. multi-scales algorithm 3 classes Sobel filter 4 classes Detection step atmospheric front Application : detection

  13. simple images basic algorithms complex images other methods Tests on multi-scale algorithm characterization of each class more details better understanding of ambiguous areas Detection step Tests on 46 areas of 14 ERS & ENVISAT images Application : detection

  14. SARTOOL wind speed Satellite data Quikscat wind MODIS sea surf. temp., fronts Meteosat visible meteo, fronts, clouds… Seawifs chlorophyll Models waves Wave Watch III sea temp., surface currents… MERCATOR France Daily tracking maps Galicia Classification step Synergetic data Application : classification

  15. @ESA ENVISAT 19 x 19 km December 9, 2002 Application : classification

  16. hs = 1 m upwellings ? SST : cold areas Classification step Application : classification

  17. Classification step backscatter strongly perturbed by atmospheric phenomenon slick not detected (high wind) slick detected (low wind) Application : classification

  18. @ESA ENVISAT 19 x 19 km December 2, 2002 Application : classification

  19. wind > 10 ms-1 hs =3-4 m SST : gradients low wind area upwelling Classification step Application : classification

  20. SAR well adapted to pollution detection not function of sunshine not function of cloud cover + high resolution wind and sea state limitation - adapted spatial coverage pollution still on surface Operational context coverage frequency regular survey delay between acquisition and analysis automaticdetection Classification synergetic data should be used give a probability SAR analysis Conclusion First step to establish an operational survey, that should speed up decision to estimate slick drift, protect coasts and fight against illegal discharges Conclusion

  21. Thank you for your attention

  22. 10 ms-1 Pavlakis et al., 1996 C X L S Ku case of high wind speed k < 400 m-1 Wavelength Measurement

  23. November 17, 2002 atmospheric front Application

  24. hs = 4-6 m wind = 7 à 10 ms-1 slick size natural slick impossible November 17, 2002 Application

  25. Wind and current model © ESA La voz de Galicia November 17, 2002 Application

  26. @ESA ENVISAT 700 m 200 m 38 x 38 km boat January 6, 2003 Application

  27. hs = 5 m slick size wind > 5 ms-1 v natural slick low wind area pollution slick size straight shape boat January 6, 2003 Application