Oil spill detection by a support vector machine based on polarization decomposition characteristics
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Oil spill detection by a support vector machine based on polarization decomposition characteristics ZOU Yarong1, 2, SHI Lijian1, 2*, ZHANG Shengli3, LIANG Chao1, 2, ZENG Tao1, 2 1 National Satellite Ocean Application Service, State Oceanic Adminstration, Beijing 100081, China 2 Key Laboratory for Space Ocean Remote Sensing and Application, State Oceanic Administration, Beijing 100081,
China 3 School of English Language, Literature and Culture, Beijing International Studies University, Beijing 100024, China
Received 24 August 2015; accepted 2 November 2015 ©The Chinese Society of Oceanography and Springer-Verlag Berlin Heidelberg 2016
Abstract
Marine oil spills have caused major threats to marine environment over the past few years. The early detection of the oil spill is of great significance for the prevention and control of marine disasters. At present, remote sensing is one of the major approaches for monitoring the oil spill. Full polarization synthetic aperture radarc SAR data are employed to extract polarization decomposition parameters including entropy (H) and reflection entropy (A). The characteristic spectrum of the entropy and reflection entropy combination has analyzed and the polarization characteristic spectrum of the oil spill has developed to support remote sensing of the oil spill. The findings show that the information extracted from (1–A)×(1–H) and (1–H)×A parameters is relatively evident effects. The results of extraction of the oil spill information based on H×A parameter are relatively not good. The combination of the two has something to do with H and A values. In general, when H>0.7, A value is relatively small. Here, the extraction of the oil spill information using (1–A)×(1–H) and (1–H)×A parameters obtains evident effects. Whichever combined parameter is adopted, oil well data would cause certain false alarm to the extraction of the oil spill information. In particular the false alarm of the extracted oil spill information based on (1–A)×(1–H) is relatively high, while the false alarm based on (1–A)×H and (1–H)×A parameters is relatively small, but an image noise is relatively big. The oil spill detection employing polarization characteristic spectrum support vector machine can effectively identify the oil spill information with more accuracy than that of the detection method based on single polarization feature. Key words: oil spill, polarization synthetic aperture radar, characteristic spectrum, entropy, reflection entropy, support vector machine Citation: Zou Yarong, Shi Lijian, Zhang Shengli, Liang Chao, Zeng Tao. 2016. Oil spill detection by a support vector machine based on polarization decomposition characteristics. Acta Oceanologica Sinica, 35(9): 86–90, doi: 10.1007/s13131-016-0935-5
1 Introduction Oil spill accidents are increasing with the development of marine transport and operation of more marine oil fields. These accidents usually lead to large area oil spill in seas and coastal waters, resulting in major and sometimes devastating impacts on marine ecosystems. Therefore
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