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A stochastic model for oil spill detection in marine environment with SAR data

机译:具有SAR数据的海洋环境中的油漏检测随机模型

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The catastrophic explosion which destroyed the Deep Water Horizon (DWH) oil rig in the Gulf of Mexico, in April 2010, caused, in a period of three months, the discharge of some 4.9 million barrels of oil. The DWH remains the largest accidental marine oil spill in the history of petroleum industry. In order to detect the oil slick, and to measure its extent and geo-location, we present a methodology based on the use of SAR images and of stochastic process theory. The task of scene interpretation makes use of pixel potential functions supported by the Ising model. The scheme is applied to an ASAR image of the Gulf of Mexico, at the time when the DWH oil spill had already completely developed. The result shows both the labelled field of the image elements and the extent of the oil spill itself. The MRF model and the parameters of the stochastic optimization procedure are fully described.
机译:灾难性的爆炸,摧毁了墨西哥湾的深水地平线(DWH)石油钻井平台,于2010年4月,在三个月内造成的,在三个月内排放约490万桶油。 DWH仍然是石油工业史上最大的意外海洋石油泄漏。为了检测油烟,并测量其范围和地理位置,我们提出了一种基于SAR图像和随机过程理论的方法。场景解释的任务利用了ISING模型支持的像素电位功能。该方案应用于墨西哥湾的ASAR图像,当时DWH漏油已经完全开发。结果显示了图像元素的标记字段和漏油本身的范围。充分描述了MRF模型和随机优化过程的参数。

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