Publikasjonsdetaljer
Tidsskrift: Proceedings of SPIE, the International Society for Optical Engineering, vol. 9244, p. 14, 2014
Utgivere: SPIE - The International Society for Optics and Photonics
Internasjonale standardnumre:
Trykt: 0277-786X
Elektronisk: 1996-756X
Lenker:
DOI: doi.org/10.1117/12.2067474
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.