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Proceedings Paper

Classification of ocean surface slicks in hybrid-polarimetric SAR data
Author(s): Arnt B. Salberg; Siri Ø. Larsen; Robert Jenssen
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Paper 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.

Paper Details

Date Published: 23 October 2014
PDF: 14 pages
Proc. SPIE 9244, Image and Signal Processing for Remote Sensing XX, 92440K (23 October 2014); doi: 10.1117/12.2067474
Show Author Affiliations
Arnt B. Salberg, Norwegian Computing Ctr. (Norway)
Siri Ø. Larsen, Norwegian Computing Ctr. (Norway)
Robert Jenssen, Univ. of Tromsø (Norway)

Published in SPIE Proceedings Vol. 9244:
Image and Signal Processing for Remote Sensing XX
Lorenzo Bruzzone, Editor(s)

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