Testing the Generalization Efficiency of Oil Slick Classification Algorithm Using Multiple Sar Data for Deepwater Horizon Oil Spill

Abstract

Abstract. Marine oil spills due to releases of crude oil from tankers, offshore platforms, drilling rigs and wells, etc. are seriously affecting the fragile marine and coastal ecosystem and cause political and environmental concern. A catastrophic explosion and subsequent fire in the Deepwater Horizon oil platform caused the platform to burn and sink, and oil leaked continuously between April 20th and July 15th of 2010, releasing about 780,000 m3 of crude oil into the Gulf of Mexico. Today, space-borne SAR sensors are extensively used for the detection of oil spills in the marine environment, as they are independent from sun light, not affected by cloudiness, and more cost-effective than air patrolling due to covering large areas. In this study, generalization extent of an object based classification algorithm was tested for oil spill detection using multiple SAR imagery data. Among many geometrical, physical and textural features, some more distinctive ones were selected to distinguish oil and look alike objects from each others. The tested classifier was constructed from a Multilayer Perception Artificial Neural Network trained by ABC, LM and BP optimization algorithms. The training data to train the classifier were constituted from SAR data consisting of oil spill originated from Lebanon in 2007. The classifier was then applied to the Deepwater Horizon oil spill data in the Gulf of Mexico on RADARSAT-2 and ALOS PALSAR images to demonstrate the generalization efficiency of oil slick classification algorithm.

On the Performance of Filters for Reduction of Speckle Noise in SAR Images Off the Coast of the Gulf of Guinea

“…SAR is frequently used for oil spill detection at sea, compared to the various satellite instruments. The sea-surface echo radar signal is modulated by the wind-induced capillary waves, and thus carries detailed information on the surface roughness [6], giving the degree of oil spill on the surface of the sea. Due to the difference in the surface tensions of water and oil, there is also a difference in the backscattered signals that is received by the instrument.Oil film on the ocean surface has higher surface tension than water, and doesreduce the surface capillary waves, resulting in a smoother sea surface.…”

“…However, other natural phenomenon also contributes to damping the short waves and creates dark areas of the sea surface in the SAR images. Some of these natural phenomenons that create dark areas on the sea surface are due to suspension of Bragg scattering mechanism depending on ocean and/or atmospheric conditions [6]. Atmospheric conditions also contribute to noise in the SAR images, which affects the interpretation and/or feature extraction from the images.…”