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. The gushing wellhead was capped only after 87 days, on July 15, 2010. 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. Thanks to a European Space Agency (ESA) project for studying ocean phenomena, among the various SAR products covering the DWH accident, the Envisat/ASAR wide swath (WS) ones were chosen; in particular, two significative ASAR WS images were selected: the first available after the accident, on April 26, and the second one when the oil spill was already fully developed, on May 2. 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. While speckle can even be exploited to analyze SAR oil spills at full resolution,3 it generally causes difficulties in image interpretation and is usually removed with specialized filters4,5 but at the risk of degrading the spatial resolution. Heavily oil-polluted ocean surfaces provide a specular reflection and a reduced Bragg scattering. Compared with the semispecular backscatter of the open sea surfaces, pixels of oil spills produce darker signatures in SAR images. Several other natural phenomena, such as low tides, low wind areas, biogenic material, and oceanic or atmospheric fronts, produce the so-called look-alikes of oil slicks.6,7 Various papers have been presented in the literature describing semiautomatic parametric and nonparametric algorithms for oil spill detection, for instance, adaptive thresholding segmentation methods,8,9 neural networks,10 fractal algorithms,11 and multiscale wavelet representations.12 A different group of algorithms based on geometric and statistical features have also been used,13 while those based on the physical modeling of complex systems require oil viscoelastic properties and external data such as scatterometer wind fields.14 Finally, a recent review paper15 provides a comprehensive analysis of all the issues related to oil spill detection with SAR images. A relevant paper16 has demonstrated that only dual- and/or full-polarimetric SAR images can give precise oil spill detection, clearly distinguishing between oil and look-alikes; in the case of single-polarimetric images, the same objective can only be attained if external information is attached to the image.17 Unfortunately, regular acquisitions by SAR observing satellites are mainly single-polarimetric, while dual- and/or full-polarimetric ASAR images are extremely rare in the ESA archive. 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 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 segmentation process is modeled by taking into account the Bayes formulation. The optimization of the probability functions by means of the Markov random field (MRF) theory is defined by the maximum a posteriori (MAP) criterion. The stochastic minimization algorithm is modified in order to update both the parameters of the a priori label model and its system neighborhood.