Confidence levels in the detection of oil spills from satellite imagery: from research to the operational use

Guido Ferraro; Olaf Trieschmann; Marko Perkovic; Dario Tarchi

doi:10.1117/12.977947

21 November 2012 Confidence levels in the detection of oil spills from satellite imagery: from research to the operational use

Guido Ferraro, Olaf Trieschmann, Marko Perkovic, Dario Tarchi

Author Affiliations +

Proceedings Volume 8536, SAR Image Analysis, Modeling, and Techniques XII; 85360G (2012) https://doi.org/10.1117/12.977947
Event: SPIE Remote Sensing, 2012, Edinburgh, United Kingdom

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.

Citation Download Citation

Guido Ferraro, Olaf Trieschmann, Marko Perkovic, and Dario Tarchi "Confidence levels in the detection of oil spills from satellite imagery: from research to the operational use", Proc. SPIE 8536, SAR Image Analysis, Modeling, and Techniques XII, 85360G (21 November 2012); https://doi.org/10.1117/12.977947

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