During emergency responses to oil spills on the sea surface, quick detection and characterization of an oil slick is essential. The use of Synthetic Aperture Radar (SAR) in general and polarimetric SAR (PolSAR) in particular to detect and discriminate mineral oils from look-alikes is known. However, research exploring its potential to detect oil slick characteristics, e.g., thickness variations, is relatively new. Here a Multi-Source Image Processing System capable of processing optical, SAR and PolSAR data with proper statistical models was tested for the first time for oil slick characterization. An oil seep detected by NASA`s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) in the Gulf of Mexico was used as a study case. This classifier uses a supervised approach to compare stochastic distances between different statistical distributions (fx) and hypothesis tests to associate confidence levels to the classification results. The classifier was able to detect zoning regions within the slick with high global accuracies and low uncertainties. Two different classes, likely associated with the thicker and thinner oil layers, were recognized. The best results, statistically equivalent, were obtained using different data formats: polarimetric, intensity pair and intensity single-channel. The presence of oceanic features in the form of oceanic fronts and internal waves created convergence zones that defined the shape, spreading and concentration of the thickest layers of oil. The statistical classifier was able to detect the thicker oil layers accumulated along these features. Identification of the relative thickness of spilled oils can increase the oil recovery efficiency, allowing better positioning of barriers and skimmers over the thickest layers. Decision makers can use this information to guide aerial surveillance, in situ oil samples collection and clean-up operations in order to minimize environmental impacts.

1141194: During oil spills response activities (OSR), discrimination and characterization of oil slicks has the potential to minimize false alarms and to indicate the location of thicker layers of oil, providing tactical and strategic information to decision makers. This specific demand for spatial intelligence during emergencies emphasizes the importance of the Multisource Image Processing System developed by the Brazilian Institute for Space Research (INPE) and described herein. Synthetic Aperture Radars (SAR) are the most common operational remote sensing data providers, acquiring images in different frequencies, incidence angles, resolutions, polarizations and formats (amplitude, intensity or complex). The system is able to integrate this product diversity including polarized (SAR), polarimetric (PolSAR) and optical data acquired by multiple sensors with different signal to noise ratios (SNR) and statistical distributions. The classifier uses a supervised approach to compare stochastic distances between different statistical distributions and statistical hypothesis testing to associate confidence levels to the results. Two study cases including mineral and biogenic oils with internal thickness variations were used to demonstrate the potential of the system to process SAR and PolSAR data, considering different formats and statistical distributions. The system was able to differentiate mineral oils from biogenic oils, as well as to detect thicker layers within the oil slicks with high global accuracies and low uncertainties. The polarimetric (dual and full-pol) and the intensity pair data had better accuracy at discriminating different types of oils; meanwhile SAR and PolSAR data returned statistically equivalent global accuracies in characterizing oil slicks. The low noise floor in UAVSAR sensor is able to detect oil thickness variations, which is feasible even if only using the VV channel. The possibility to qualitatively identify the thicker layers of the spilled oil, as well as minimize false alarms ratio is a significant contribution to ongoing efforts to improve OSR. The classification maps provided by the system may also be used to extract the relative areas for each type of oil, supporting the oil volume estimation. In this context, the integration of all information extracted from SAR and PolSAR data is useful both at strategic and tactical levels of an emergency, and is being used to guide aerial surveillance and the deployment of barriers and skimmers, aiming to increase oil recovery efficiency and minimize environmental impact.