Abstract
Marine oil spill is a major threat to marine and coastal ecosystems and is seen relatively often, such as the Deepwater Horizon oil spill disaster in the Gulf of Mexico in 2010 and Bohai Sea oil spills in China in 2011. Fast and accurate discrimination of oil spill is the largest challenge in detection of oil spills using remote sensing technology. In this research imaging spectroscopic analysis and Oil Slope Index(OSI) were developed to map the locations of surface crude oil in Gulf of Mexico using the SpecTIR data which was collected at 2.2m GSD and 360 spectral channels, covering 390–2450nm. The spectral features and differences of the main objects of oil, sea water and clouds can be found in the DN value of pixel spectra. The slope difference in the range from 550nm to 750nm between crude oil and other objects can be taken as a key feature for detection of crude oil on the sea surface. The Oil Slope Index(OSI) avoids the absorption bands of O <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> and H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> O in the air and transforms the imaging spectroscopy data into a single band image that shows the distribution of crude oil spill. OSI values can be easily calculated from radiance or DN data and no additional pre-processing of the imagery was necessary before crude oil detection. The result shows that the algorithms work well for oil spill detection which integrated the spectral feature of oil, sea water and clouds by establishing a decision tree. The automatic determination of thresholds by applying Otsu's image segmentation can realize the fast and automatic extraction of surface crude oil. This study demonstrated that the Oil Slope Index (OSI) has the potential to become a useful image processing algorithm and operational tool for imaging spectroscopy detection of crude oil spill.
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