Oil spill detection with fully polarimetric UAVSAR data |
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Authors: | Liu Peng Li Xiaofeng Qu John J Wang Wenguang Zhao Chaofang Pichel William |
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Institution: | aORSI, Ocean University of China, Qingdao, Shandong 266100, China;bESTC, George Mason University, Fairfax, VA 22030, United States;cIMSG at NOAA/NESDIS, Camp Springs, MD 20746, United States;dSchool of EIE, Beihang University, Beijing 100191, China;eNOAA/NESDIS/STAR, Camp Springs, MD 20746, United States |
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Abstract: | In this study, two ocean oil spill detection approaches based on four scattering matrices measured by fully polarimetric synthetic aperture radar (SAR) are presented and compared. The first algorithm is based on the co-polar correlation coefficient, ρ, and the scattering matrix decomposition parameters, Cloud entropy (H), mean scattering angle (α) and anisotropy (A). While each of these parameters has oil spill signature in it, we find that combining these parameters into a new parameter, F, is a more effective way for oil slick detection. The second algorithm uses the total power of four polarimetric channels image (SPAN) to find the optimal representation of the oil spill signature. Otsu image segmentation method can then be applied to the F and SPAN images to extract the oil slick features. Using the L-band fully polarimetric Uninhabited Aerial Vehicle – synthetic aperture radar (UAVSAR) data acquired during the 2010 Deepwater Horizon oil spill disaster event in the Gulf of Mexico, we are able to successfully extract the oil slick information in the contaminated ocean area. Our result shows that both algorithms perform well in identifying oil slicks in this case. |
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Keywords: | Oil spill detection Synthetic aperture radar Polarimetric UAVSAR |
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