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Proceedings Paper

Feature-based and statistical methods for analyzing the Deepwater Horizon oil spill with AVIRIS imagery
Author(s): Robert S. Rand; Roger N. Clark; K. Eric Livo
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Paper Abstract

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.

Paper Details

Date Published: 7 September 2011
PDF: 12 pages
Proc. SPIE 8158, Imaging Spectrometry XVI, 81580N (7 September 2011); doi: 10.1117/12.894909
Show Author Affiliations
Robert S. Rand, National Geospatial-Intelligence Agency (United States)
Roger N. Clark, U.S. Geological Survey (United States)
K. Eric Livo, U.S. Geological Survey (United States)


Published in SPIE Proceedings Vol. 8158:
Imaging Spectrometry XVI
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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