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Proceedings Paper

Identification and detection of oil and oil-derived substances at the surface and subsurface levels via hyperspectral imaging
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Paper Abstract

Detection and estimation of oil and oil-derived substances from an oil spill is a challenging issue. Over the last few years, several algorithms have been proposed for the detection of oil on the ocean surface. These techniques do not address the issue of detection of subsurface oil and estimate the depth of the location of oil at the subsurface level. In this paper, algorithms are developed to detect the presence of surface oil in ocean water using hyperspectral imagery. A support vector machine classifier was trained using region-of-interests (ROIs) to classify the oil/oil-derived substances under the water surface in the Gulf of Mexico. Using the pixel intensity of the identified oil based image, Beer-Lambert's law is used to calculate the depth at which the oil and/or oil-derived substance are present in the scene of investigation.

Paper Details

Date Published: 23 April 2012
PDF: 13 pages
Proc. SPIE 8398, Optical Pattern Recognition XXIII, 839802 (23 April 2012); doi: 10.1117/12.921314
Show Author Affiliations
M. S. Alam, Univ. of South Alabama (United States)
R. P. Gollapalli, Univ. of South Alabama (United States)
P. Sidike, Univ. of South Alabama (United States)


Published in SPIE Proceedings Vol. 8398:
Optical Pattern Recognition XXIII
David P. Casasent; Tien-Hsin Chao, Editor(s)

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