Detection of oil floating on the ocean surface, and particularly thick layers of it, is crucial for emergency response to oil spills. While detection of oil on the ocean surface is possible under moderate sea-state conditions using a variety of remote-sensing methods, estimation of oil layer thickness is technically challenging. In this paper, we used synthetic aperture radar (SAR) imagery collected during the Deepwater Horizon oil spill and the Texture Classifier Neural Network Algorithm (TCNNA) to identify the spill’s extent. We then developed an oil emulsion detection algorithm using TCNNA outputs to enhance the contrast of pixels within the oil slick in order to identify SAR image signatures that may correspond to regions of thick, emulsified oil. These locations were found to be largely consistent with ship-based observations and optical and thermal remote-sensing instrument data. The detection method identifies regions of increased radar backscattering within larger areas of oil-covered water. Detection was dependent on SAR incident angles and SAR beam mode configuration. L-band SAR was found to have the largest window of incidence angles (19–38° off nadir) useful for detecting oil emulsions. C-band SAR showed a narrower window (20–32° off nadir) than L-band, while X-band SAR had the narrowest window (20–31° off nadir). The results suggest that in case of future spills in the ocean, SAR data may be used to identify oil emulsions to help make management decisions.