Identification and detection of oil and oil-derived substances at the surface and subsurface levels via hyperspectral imaging
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. © 2012 SPIE.
Proceedings of SPIE - The International Society for Optical Engineering
Alam, M., Gollapalli, R., & Sidike, P. (2012). Identification and detection of oil and oil-derived substances at the surface and subsurface levels via hyperspectral imaging. Proceedings of SPIE - The International Society for Optical Engineering. Retrieved from https://ir.una.edu/engi_facpub/7