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Remote Sens. 2015, 7(1), 1112-1134;

Oil Spill Detection in Glint-Contaminated Near-Infrared MODIS Imagery

CNR-ISAC UOS Roma, Via Fosso Del Cavaliere 100, 00133 Roma, Italy
Author to whom correspondence should be addressed.
Academic Editors: Deepak Mishra, Eurico J. D’Sa, Sachidananda Mishra and Prasad S. Thenkabail
Received: 2 October 2014 / Accepted: 12 January 2015 / Published: 19 January 2015
(This article belongs to the Special Issue Remote Sensing of Water Resources)
Full-Text   |   PDF [6823 KB, uploaded 20 January 2015]   |  


We present a methodology to detect oil spills using MODIS near-infrared sun glittered radiance imagery. The methodology was developed by using a set of seven MODIS images (training dataset) and validated using four other images (validation dataset). The method is based on the ratio image R = L'GN/LGN, where L'GN is the MODIS-retrieved normalized sun glint radiance image and LGN the same quantity, but obtained from the Cox and Munk isotropic (independent of wind direction) sun glint model. We show that in the R image, while clean water pixel values tend to one, oil spills stand out as anomalies. Moreover, we provide a criterion to distinguish between positive and negative oil-water contrast. A pixel in an R image is classified as a potential oil spill or water via a variable threshold Rs as a function of L'GN, where the threshold values are obtained from the slicks of our training dataset. Two different fitting curves are provided for Rs, according to the contrast sign. The selection of the correct fitting curve is based on the contrast type, resulting from the criterion above. Results indicate that the thresholding is able to isolate the spills and that the spills of the validation dataset are successfully detected. Spurious look-alike features, such as clouds, and other non-spill features, e.g., large areas at the glint region border, are also detected as oil spills and must be eliminated. We believe that our methodology represents a novel and promising, though preliminary, approach towards automatic oil spill detection in optical satellite images. View Full-Text
Keywords: oil spill detection; MODIS; sun glint; remote sensing techniques oil spill detection; MODIS; sun glint; remote sensing techniques

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Pisano, A.; Bignami, F.; Santoleri, R. Oil Spill Detection in Glint-Contaminated Near-Infrared MODIS Imagery. Remote Sens. 2015, 7, 1112-1134.

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