| 结合极化分解特征的SVM溢油检测研究 |
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| 引用本文： | 邹亚荣,石立坚,张胜利,梁超,曾韬.结合极化分解特征的SVM溢油检测研究[J].海洋学报(英文版),2016,35(9):86-90. |
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| 作者姓名： | 邹亚荣 石立坚 张胜利 梁超 曾韬 |
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| 作者单位： | 国家卫星海洋应用中心, 北京, 100081;国家海洋局空间遥感与应用研究重点实验室, 北京, 100081,国家卫星海洋应用中心, 北京, 100081;国家海洋局空间遥感与应用研究重点实验室, 北京, 100081,北京第二外国语学院, 北京, 100024,国家卫星海洋应用中心, 北京, 100081;国家海洋局空间遥感与应用研究重点实验室, 北京, 100081,国家卫星海洋应用中心, 北京, 100081;国家海洋局空间遥感与应用研究重点实验室, 北京, 100081 |
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| 摘 要： | 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 H0.7,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.
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| 关 键 词： | 溢油 极化SAR 特征谱 熵 反熵 SVM |
| 收稿时间： | 2015/8/24 0:00:00 |
| 修稿时间： | 2015/11/2 0:00:00 |
Oil spill detection by a support vector machine based on polarization decomposition characteristics |
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| ZOU Yarong,SHI Lijian,ZHANG Shengli,LIANG Chao and ZENG Tao.Oil spill detection by a support vector machine based on polarization decomposition characteristics[J].Acta Oceanologica Sinica,2016,35(9):86-90. |
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| Authors: | ZOU Yarong SHI Lijian ZHANG Shengli LIANG Chao and ZENG Tao |
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| Institution: | 1.National Satellite Ocean Application Service, State Oceanic Adminstration, Beijing 100081, China;Key Laboratory for Space Ocean Remote Sensing and Application, State Oceanic Administration, Beijing 100081, China2.School of English Language, Literature and Culture, Beijing International Studies University, Beijing 100024, China |
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| 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. |
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| Keywords: | oil spill polarization synthetic aperture radar characteristic spectrum entropy reflection entropy support vector machine |
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