Abstract
A binary segmentation scheme, based on the Markov random field theory, is pre- sented. In order to obtain a more integrated label field, the simulated annealing schedule is modi- fied for performing a joint conditional estimation of model parameters. To reach a finer detection, the pixel neighborhood system of the a priori model is continuously updated at each cycle of the optimization algorithm. Maximum a posteriori is the central criterion of these algorithms. The proposed processing scheme is applied to a sequence of Envisat/ ASAR images of the Deepwater Horizon disaster of the Gulf of Mexico in the spring of 2010. Initial oil spills statistical parameters are extracted by visual analysis, but they are updated during the minimization cycles. The proposed scheme, when compared with a conventional Markov random field one, provides a better detection of fine structures. In addition, facing the complex ocean phenomena reflected in the synthetic aperture radar images, the final label field results are extremely well defined. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. (DOI: 10
Highlights
The Deepwater Horizon (DWH) oil spill in the Gulf of Mexico, which flowed unabated for three months in 2010, was the largest accidental marine oil spill in the history of the petroleum industry; its source was a sea-floor oil gusher resulting from the April 20, 2010, DWH explosion which claimed 11 lives
This paper presents a processing scheme based on binary segmentation whose aim is to provide an efficient tool to measure the marine oil spill extent in synthetic aperture radar (SAR) images; for the reasons explained above, it was decided to apply the processing scheme to single-polarimetric images, with an approach that only makes use of the radiometric information of the SAR scene
The initial distribution of samples takes into account two training windows of 30 × 30 pixels, which are assigned at pattern regions of classes w1 and w2, where w1 is the sea class and w2 is the oil spill class
Summary
The Deepwater Horizon (DWH) oil spill in the Gulf of Mexico, which flowed unabated for three months in 2010, was the largest accidental marine oil spill in the history of the petroleum industry; its source was a sea-floor oil gusher resulting from the April 20, 2010, DWH explosion which claimed 11 lives. Beginning with the first days after the accident, all the Earth observing satellites focused their image acquisitions over the Gulf of Mexico. Among the many sensors on board the satellites, the synthetic aperture radar (SAR) is certainly the most powerful one for imaging different ocean phenomena, like waves, surface winds, oil spills, and sea-ice in all-weather conditions. The interaction of the highly coherent radiation of a radar signal with the ocean elements combined with the atmospheric conditions produces a very complex backscatter.[1,2] The geophysical system collaterally creates the speckle phenomenon, which produces the characteristic grainy appearance of SAR images. Oil-polluted ocean surfaces provide a specular reflection and a reduced Bragg scattering
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