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Oil slick detection by SAR imagery : application on Prestige accident images. Fanny GIRARD-ARDHUIN 1,2 , F. COLLARD 1 , G. MERCIER 2 et R. GARELLO 2 1 BOOST technologies, Brest 2 GET-ENST Bretagne, TAMCIC 2658 CNRS, Brest. Introduction. Image analysis. Measurement. Application .

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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

Fanny GIRARD-ARDHUIN1,2, F. COLLARD1,

G. MERCIER2 et R. GARELLO2

1 BOOST technologies, Brest

2 GET-ENST Bretagne, TAMCIC 2658 CNRS, Brest

slide2

Introduction

Image analysis

Measurement

Application

Detection

Sensors

Case study

Slick detection and classification

Classification

Parameters

Methodology

Conclusion

Outline

slide3

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

slide4

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

slide5

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

slide6

slick

dark patch

Masuko et al., 1995

C, X and Ku-bands

Wismann, 1993

Wavelength

Measurement

slide7

Wismann, 1993

high elasticity

oil slick

easier to detect

thick

Slick nature

Measurement

slide8

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

slide9

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

slide10

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

slide11

ENVISAT

@ESA

38 x 38 km

November 17, 2002

tanker

Application : detection

slide12

multi-scales algorithm

3 classes

Sobel filter

4 classes

Detection step

atmospheric front

Application : detection

slide13

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

slide14

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

slide15

@ESA

ENVISAT

19 x 19 km

December 9, 2002

Application : classification

slide16

hs = 1 m

upwellings ?

SST : cold areas

Classification step

Application : classification

slide17

Classification step

backscatter strongly perturbed by atmospheric phenomenon

slick not detected (high wind)

slick detected

(low wind)

Application : classification

slide18

@ESA

ENVISAT

19 x 19 km

December 2, 2002

Application : classification

slide19

wind > 10 ms-1

hs =3-4 m

SST : gradients

low wind area

upwelling

Classification step

Application : classification

slide20

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

slide22

10 ms-1

Pavlakis et al., 1996

C

X

L

S

Ku

case of high wind speed

k < 400 m-1

Wavelength

Measurement

slide23

November 17, 2002

atmospheric front

Application

slide24

hs = 4-6 m

wind = 7 à 10 ms-1

slick size

natural slick impossible

November 17, 2002

Application

slide25

Wind and current model

© ESA

La voz de Galicia

November 17, 2002

Application

slide26

@ESA

ENVISAT

700 m

200 m

38 x 38 km

boat

January 6, 2003

Application

slide27

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