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

Robert S. Rand; Roger N. Clark; K. Eric Livo

doi:10.1117/12.894909

6 September 2011 Feature-based and statistical methods for analyzing the Deepwater Horizon oil spill with AVIRIS imagery

Robert S. Rand, Roger N. Clark, K. Eric Livo

Author Affiliations +

Proceedings Volume 8158, Imaging Spectrometry XVI; 81580N (2011) https://doi.org/10.1117/12.894909
Event: SPIE Optical Engineering + Applications, 2011, San Diego, California, United States

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.

Citation Download Citation

Robert S. Rand, Roger N. Clark, and K. Eric Livo "Feature-based and statistical methods for analyzing the Deepwater Horizon oil spill with AVIRIS imagery", Proc. SPIE 8158, Imaging Spectrometry XVI, 81580N (6 September 2011); https://doi.org/10.1117/12.894909

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available