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Remote Sens. 2017, 9(8), 799;

Ocean Oil Spill Classification with RADARSAT-2 SAR Based on an Optimized Wavelet Neural Network

School of Geosciences, China University of Petroleum, Qingdao 266580, China
Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao 266071, China
Graduate School, China University of Petroleum, Qingdao 266580, China
GST at National Oceanic and Atmospheric Administration (NOAA)/NESDIS, College Park, MD 20740-3818, USA
School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
Author to whom correspondence should be addressed.
Received: 20 June 2017 / Revised: 18 July 2017 / Accepted: 1 August 2017 / Published: 3 August 2017
(This article belongs to the Special Issue Radar Remote Sensing of Oceans and Coastal Areas)
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Oil spill accidents from ship or oil platform cause damage to marine and coastal environment and ecosystems. To monitor such spill events from space, fully polarimetric (Pol-SAR) synthetic aperture radar (SAR) has been greatly used in improving oil spill observation. Aiming to promote ocean oil spill classification accuracy, we developed a new oil spill identification method by combining multiple fully polarimetric SAR features data with an optimized wavelet neural network classifier (WNN). Two sets of RADARSAT-2 fully polarimetric SAR data are applied to test the validity of the developed method. The experimental results show that: (1) the convergence ability of optimized WNN can be enhanced, improving overall classification accuracy of ocean oil spill, in comparison to the classification results based on a common un-optimized WNN classifier; and (2) the joint use of the multiple fully Pol-SAR features as the inputs of the classifier can achieve better classification result than that only with single fully Pol-SAR feature. The developed method can improve classification accuracy by 4.96% and 7.75%, compared with the classification results with un-optimized WNN and only with one single fully polarimetric SAR feature. The classification overall accuracy based on the proposed approach can reach 97.67%. Experimental results have proven that the proposed approach is effective and applicable to classify the ocean oil spill. View Full-Text
Keywords: oil spill; wavelet neural network; fully polarimetric SAR; RADARSAT-2 oil spill; wavelet neural network; fully polarimetric SAR; RADARSAT-2

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Song, D.; Ding, Y.; Li, X.; Zhang, B.; Xu, M. Ocean Oil Spill Classification with RADARSAT-2 SAR Based on an Optimized Wavelet Neural Network. Remote Sens. 2017, 9, 799.

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