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Proceedings Paper

Confidence levels in the detection of oil spills from satellite imagery: from research to the operational use
Author(s): Guido Ferraro; Olaf Trieschmann; Marko Perkovic; Dario Tarchi
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Paper Abstract

Detected oil spills are usually classified according to confidence levels. Such levels are supposed to describe the probability that an observed dark feature in the satellite image is related to the actual presence of an oil spill. The Synthetic Aperture Radar (SAR) derived oil spill detection probability estimation has been explored as an intrinsic aspect of oil spill classification, which fundamentally computes the likelihood that the detected dark area is related to an oil spill. However, the SAR based probability estimation should be integrated with additional criteria in order to become a more effective tool for the End Users. As example, the key information for the final users is not the confidence level of the detection “per se” but the alert (i.e. the potential impact of the pollution and the possibility to catch the polluter red-handed) that such detection generates. This topic was deeply discussed in the framework of the R and D European Group of Experts on remote sensing Monitoring of marine Pollution (EGEMP) and a paper was published in 2010. The newly established EMSA CleanSeaNet service (2nd generation) provides the alert level connected to the detection of a potential oil spill in a satellite image based on the likelihood of being an oil spill in combination with impact and culprit information.

Paper Details

Date Published: 21 November 2012
PDF: 11 pages
Proc. SPIE 8536, SAR Image Analysis, Modeling, and Techniques XII, 85360G (21 November 2012); doi: 10.1117/12.977947
Show Author Affiliations
Guido Ferraro, European Commission Joint Research Ctr. (Italy)
Olaf Trieschmann, European Maritime Safety Agency (Portugal)
Marko Perkovic, Univ. of Ljubljana (Slovenia)
Dario Tarchi, European Commission Joint Research Ctr. (Italy)

Published in SPIE Proceedings Vol. 8536:
SAR Image Analysis, Modeling, and Techniques XII
Claudia Notarnicola; Simonetta Paloscia; Nazzareno Pierdicca, Editor(s)

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