Oil Slick Characterization Using a Statistical Region-Based Classifier Applied to UAVSAR Data

Abstract

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.

In Situ Microwave Sensors and Switching Circuit for Oil Slick Thickness Measurement

“…Thick and thin oil slick were determined in [6] using a multisource image processing system capable of processing optical, synthetic-aperture radar (SAR) and polarimetric SAR (PolSAR) data. Oil slick volume was estimated by combining airborne hyperspectral and pool experiment data in [7].…”

Oil Spill Candidate Detection Using a Conditional Random Field Model on Simulated Compact Polarimetric Imagery

“…Minchew et al [11] investigated H/A/α eigenvector decomposition parameters extracted from QP UAVSAR (uninhabited aerial vehicle SAR) data to analyze the backscattering of the Deepwater Horizon (DWH) oil spill and determined that the major eigenvalue of the coherency matrix was the most promising indicator for oil slick detection. Genovez et al [31] proposed a multi-source approach to utilize optical, single-channel SAR, and QP SAR data to distinguish oil from water and classify oil into two thick and thin layers. Espeseth et al [6] used a series of short time revisit SAR images to identify areas with relatively thick oil slicks.…”

Improved Classification Models to Distinguish Natural from Anthropic Oil Slicks in the Gulf of Mexico: Seasonality and Radarsat-2 Beam Mode Effects under a Machine Learning Approach

“…Winds also drive water circulation dynamics, transporting waters of different temperatures and salinity [35]. Despite backscattering similarities, different weathering processes may change oil physicochemical properties and, consequently, their detectability in SAR data [2,7,16,18,19]. Therefore, distinct weathering mechanisms suffered by oil seeps and spills are expected to cause differences-even if small-in the backscattering coefficients.…”