Segmentation multiéchelle de nappes d'hydrocarbure
Multiscale Oil Slicks Segmentation
OPEN ACCESS
This study focuses on the segmentation and characterization of oil slicks from Synthetic Aperture Radar (SAR) data. Viscosity notably reduces the roughness of the sea surface which takes a major part in the backscattering. Hence, an oil slick is characterized by a low-backscattered energy and appears as a dark area in images. This is the reason why most of detection algorithms are based on histogram thresholding, but they appear not satisfactory as the number of false alarms is generally high.
Since oil slicks have specific impact on ocean wave spectra (from gravity-capillary waves to the swell), we propose to use a Markovian model adapted to a multiscale description of the original image. This unsupervised segmentation method allows to take into account the different states of the sea surface through its spectra. Thanks to the mixture estimation, it is possible to statistically characterize the detected areas and then to prevent from most of false alarms.
Results of segmentation are shown with two types of scenarios. The first one concerns oil spill in the Mediterranean sea detected by the ERS SAR sensor at a resolution of 25 m. The second scenario is related to the Prestige’s wreck acquired by the Envisat ASAR sensor in a wide swath mode at a resolution of 150 m.
Résumé
Nous nous intéressons à la détection et à la caractérisation des nappes d’hydrocarbure à partir d’images Radar à Synthèse d’Ouverture (RSO). La viscosité de l’hydrocarbure atténue sensiblement la rugosité de surface qui participe majoritairement à la rétro-diffusion. En conséquence, un film visqueux est caractérisé par un déficit d’énergie rétro-diffusée et apparaît comme une zone sombre dans les images. Cependant, la plupart des techniques de détection, basée sur un seuillage d’histogramme, s’avère insatisfaisante, puisqu’elle engendre un nombre élevé de fausses alarmes.
En considérant le fait qu’un film visqueux a un impact caractéristique sur la répartition de l’énergie des vagues selon les différentes longueurs d’onde (des vagues de gravité-capillarité jusqu’à la houle), nous avons développé une méthode de segmentation markovienne adaptée à une représentation multiéchelle de l’image originale. Cette méthode permet d’obtenir une classification qui tient compte des différents états du spectre de vagues. Grâce à l’estimation des lois intervenant dans le mélange, cette méthode de segmentation permet de caractériser statistiquement les zones détectées et ainsi de se prémunir contre de nombreuses fausses alarmes.
Cette stratégie a été appliquée avec succès à des images RSO de différentes résolutions (ERS-SAR en mode PRI à 25 m et ENVISAT-ASAR en mode Wide Swath à 150 m de résolution) correspondant à des situations particulières comme le naufrage d’un navire avec un hydrocarbure lourd en Atlantique et le dégazage de cuves avec un hydrocarbure plus fluide en Méditerranée.
Hidden Markov Chain, Wavelet, Synthetic Aperture Radar, Oil Slick Detection, Multicomponent Segmentation
Mots clés
Chaînes de Markov Cachées, Ondelettes, Radar à Synthèse d’Ouverture, Détection de Nappe d’Hydrocarbure, Segmentation Multicomposante
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