%0 Journal Article
%4 sid.inpe.br/mtc-m21c/2019/02.18.11.35
%2 sid.inpe.br/mtc-m21c/2019/02.18.11.35.54
%@doi 10.3390/jmse7020036
%@issn 2077-1312
%T Oil slick characterization using a statistical region-based classifier applied to UAVSAR data
%D 2019
%8 Feb.
%9 journal article
%A Genovez, Patrícia Carneiro,
%A Jones, Cathleen E.,
%A Sant'Anna, Sidnei João Siqueira,
%A Freitas, Corina da Costa,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Jet Propulsion Laboratory (JPL), California Institute of Technology
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@electronicmailaddress genovez.oilspill@gmail.com
%@electronicmailaddress cathleen.e.jones@jpl.nasa.gov
%@electronicmailaddress sidnei.santanna@inpe.br
%@electronicmailaddress corina.freitas@gmail.com
%B Journal of Marine Science and Engineering
%V 7
%N 2
%P e36
%K oil slicks characterization, oil thickness, polarized SAR data, polarimetric SAR data (PolSAR), statistical region-based classification, uncertainty maps, UAVSAR.
%X 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 NASAs 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.
%@language en
%3 genovez_oil.pdf