@Article{GenovezJoneSantFrei:2019:OiSlCh,
author = "Genovez, Patr{\'{\i}}cia Carneiro and Jones, Cathleen E. and
Sant'Anna, Sidnei Jo{\~a}o Siqueira and Freitas, Corina da
Costa",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and Jet
Propulsion Laboratory (JPL), California Institute of Technology
and {Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Oil slick characterization using a statistical region-based
classifier applied to UAVSAR data",
journal = "Journal of Marine Science and Engineering",
year = "2019",
volume = "7",
number = "2",
pages = "e36",
month = "Feb.",
keywords = "oil slicks characterization, oil thickness, polarized SAR data,
polarimetric SAR data (PolSAR), statistical region-based
classification, uncertainty maps, UAVSAR.",
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 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.",
doi = "10.3390/jmse7020036",
url = "http://dx.doi.org/10.3390/jmse7020036",
issn = "2077-1312",
language = "en",
targetfile = "genovez_oil.pdf",
urlaccessdate = "27 abr. 2024"
}