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Feature-based and statistical methods for analyzing the Deepwater Horizon oil spill with AVIRIS imagery

January 1, 2011

The Deepwater Horizon oil spill covered a very large geographical area in the Gulf of Mexico creating potentially serious environmental impacts on both marine life and the coastal shorelines. Knowing the oil's areal extent and thickness as well as denoting different categories of the oil's physical state is important for assessing these impacts. High spectral resolution data in hyperspectral imagery (HSI) sensors such as Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) provide a valuable source of information that can be used for analysis by semi-automatic methods for tracking an oil spill's areal extent, oil thickness, and oil categories. However, the spectral behavior of oil in water is inherently a highly non-linear and variable phenomenon that changes depending on oil thickness and oil/water ratios. For certain oil thicknesses there are well-defined absorption features, whereas for very thin films sometimes there are almost no observable features. Feature-based imaging spectroscopy methods are particularly effective at classifying materials that exhibit specific well-defined spectral absorption features. Statistical methods are effective at classifying materials with spectra that exhibit a considerable amount of variability and that do not necessarily exhibit well-defined spectral absorption features. This study investigates feature-based and statistical methods for analyzing oil spills using hyperspectral imagery. The appropriate use of each approach is investigated and a combined feature-based and statistical method is proposed.

Publication Year 2011
Title Feature-based and statistical methods for analyzing the Deepwater Horizon oil spill with AVIRIS imagery
DOI 10.1117/12.894909
Authors R.S. Rand, R. N. Clark, K.E. Livo
Publication Type Article
Publication Subtype Journal Article
Series Title Proceedings of SPIE
Index ID 70036351
Record Source USGS Publications Warehouse
USGS Organization Crustal Geophysics and Geochemistry Science Center