23 October 2014 Classification of ocean surface slicks in hybrid-polarimetric SAR data
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Abstract
In this paper, we propose a strategy for ocean slick classification in SAR images operating in a hybrid-polarimetric mode. The proposed scheme is successfully applied to classify mineral and plant oil slicks in SAR data covering oil spill experiments outside Norway and the Deepwater Horizon incident in the Gulf of Mexico. Using the elements of a hybrid-polarimetric coherency matrix as features, we construct a random forest classifier from training data obtained from an SAR image covering an oil-on-water exercise in the North Sea. The results show that we area able to distinguish mineral oil from plant oil and low wind slicks, however, it is challenging to distinguish the mineral oil types emulsion and crude oil. Due to the potential of wide swath widths, we conclude that hybrid-polarity is an attractive mode for future enhanced SAR-based oil spill monitoring.
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Arnt B. Salberg, Arnt B. Salberg, Siri Ø. Larsen, Siri Ø. Larsen, Robert Jenssen, Robert Jenssen, } "Classification of ocean surface slicks in hybrid-polarimetric SAR data", Proc. SPIE 9244, Image and Signal Processing for Remote Sensing XX, 92440K (23 October 2014); doi: 10.1117/12.2067474; https://doi.org/10.1117/12.2067474
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