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Open AccessCase Report

Semi-Automatic Oil Spill Detection on X-Band Marine Radar Images Using Texture Analysis, Machine Learning, and Adaptive Thresholding

by Peng Liu 1, Ying Li 2,*, Bingxin Liu 1, Peng Chen 1 and Jin Xu 1
1
Navigation College, Dalian Maritime University, Dalian 116026, China
2
Environmental Information Institute, Dalian Maritime University, Dalian 116026, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(7), 756; https://doi.org/10.3390/rs11070756
Received: 17 March 2019 / Revised: 25 March 2019 / Accepted: 26 March 2019 / Published: 28 March 2019
(This article belongs to the Special Issue Oil Spill Remote Sensing)
Oil spills bring great damage to the environment and, in particular, to coastal ecosystems. The ability of identifying them accurately is important to prompt oil spill response. We propose a semi-automatic oil spill detection method, where texture analysis, machine learning, and adaptive thresholding are used to process X-band marine radar images. Coordinate transformation and noise reduction are first applied to the sampled radar images, coarse measurements of oil spills are then subjected to texture analysis and machine learning. To identify the loci of oil spills, a texture index calculated by four textural features of a grey level co-occurrence matrix is proposed. Machine learning methods, namely support vector machine, k-nearest neighbor, linear discriminant analysis, and ensemble learning are adopted to extract the coarse oil spill areas indicated by the texture index. Finally, fine measurements can be obtained by using adaptive thresholding on coarsely extracted oil spill areas. Fine measurements are insensitive to the results of coarse measurement. The proposed oil spill detection method was used on radar images that were sampled after an oil spill accident that occurred in the coastal region of Dalian, China on 21 July 2010. Using our processing method, thresholds do not have to be set manually and oil spills can be extracted semi-automatically. The extracted oil spills are accurate and consistent with visual interpretation. View Full-Text
Keywords: X-band marine radar; oil spill detection; texture analysis; machine learning; adaptive algorithm X-band marine radar; oil spill detection; texture analysis; machine learning; adaptive algorithm
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MDPI and ACS Style

Liu, P.; Li, Y.; Liu, B.; Chen, P.; Xu, J. Semi-Automatic Oil Spill Detection on X-Band Marine Radar Images Using Texture Analysis, Machine Learning, and Adaptive Thresholding. Remote Sens. 2019, 11, 756.

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