Detection and object-based classification of offshore oil slicks using ENVISAT-ASAR images

Abstract

The aim of this study is to propose and test a multi-level methodology for detection of oil slicks in ENVISAT Advanced Synthetic Aperture Radar (ASAR) imagery, which can be used to support the identification of hydrocarbon seeps. We selected Andrusov Ridge in the Central Black Sea as the test study area where extensive hydrocarbon seepages were known to occur continuously. Hydrocarbon seepage from tectonic or stratigraphic origin at the sea floor causes oily gas plumes to rise up to the sea surface and form thin oil films called oil slicks. Microwave sensors like synthetic aperture radar (SAR) are very suitable for ocean remote sensing as they measure the backscattered radiation from the surface and show the roughness of the terrain. Oil slicks dampen the sea waves creating dark patches in the SAR image. The proposed and applied methodology includes three levels: visual interpretation, image filtering and object-based oil spill detection. Level I, after data preparation with visual interpretation, includes dark spots identification and subsets/scenes creation. After this process, the procedure continues with categorization of subsets/scenes into three cases based on contrast difference of dark spots to the surroundings. In level II, by image and morphological filtering, it includes preparation of subsets/scenes for segmentation. Level III includes segmentation and feature extraction which is followed by object-based classification. The object-based classification is applied with the fuzzy membership functions defined by extracted features of ASAR subsets/scenes, where the parameters of the detection algorithms are tuned specifically for each case group. As a result, oil slicks are discriminated from look-alikes with an overall classification accuracy of 83% for oil slicks and 77% for look-alikes obtained by averaging three different cases.

Modelling of oil spill frequency, leak sources and contamination probability in the Caspian Sea using multi-temporal SAR images 2006–2010 and stochastic modelling

“…Detection of oil spills caused by anthropogenic oil exploration activities plays a significant role in the environmental management of cleaning activities and response to emergency situations. Detection of natural seepage is considered to be one of the significant preliminary works for offshore petroleum exploration (Brekke & Solberg 2005;Akar et al 2011). The oil slick detection and mapping were often used for the hydrocarbon exploration activities and emergency response to oil spill incidents in the world places such as Gulf of Mexico (Friedman et al 2002) and South Caspian Sea (Ivanov & Vostokov et al 2004; *Corresponding author.…”

“…It is well known that microwave sensors have the advantage for oil slick monitoring due to its wide area coverage and day and night capabilities (Martinez & Moreno, 1996;Ziemke 1996;Kubat et al 1998;Del Frate et al 2000;Topouzelis et al 2002;Topouzelis et al 2007;Akar et al 2011). Radar backscattering level decreases with slick and appears as a dark patch with lower backscatter from the sea surface (Girard-Ardhuin et al 2003).…”

“…Discrimination of oil slicks from look-alikes is performed based on the textural, geometrical, radiometric, persistence and temporal repetition (Solberg et al 1999;Brekke & Solberg 2005;Karantzalos & Argialas 2008;Topouzelis 2008;Akar et al 2011).…”

Detection of Oil Pollution Hotspots and Leak Sources Through the Quantitative Assessment of the Persistence and Temporal Repetition of Regular Oil Spills in the Caspian Sea Using Remote Sensing and Gis

“…It is well known that microwave sensors have the advantage for oil slick monitoring due to its wide area coverage and day and night capabilities (Akar et al 2011;Martinez et al 1996;Ziemke 1996;Kubat et al 1998;Del Frate et al 2000;Topouzelis et al 2002;Topouzelis et al 2007). Radar backscattering level decreases with slick and appears as a dark patch with lower backscatter from the sea surface (Girard-Ardhuin et al 2003).…”

“…The discrimination of oil slicks from oil spill look-alikes produced by oceanic and atmospheric processes is one of the most complex issues in the process of oil slick detection from SAR images. Discrimination of oil slicks from look-alikes is performed based on the textural, geometrical, radiometric, persistence and temporal repetition (Akar et al 2011;Brekke et al 2005;Karantzalos et al 2008;Topouzelis 2008;Solberg et al 1999). The main goal of this research was to study oil pollution around the Oil Rocks Settlement, Chilov and Pirallahi Islands in the Caspian Sea using ENVISAT Advanced Synthetic Aperture Radar (ASAR) Wide Swath Medium Resolution satellite images acquired during the period of 2006-2010.…”