| ZOU Yarong,SHI Lijian,ZHANG Shengli,LIANG Chao,ZENG Tao.Oil spill detection by a support vector machine based on polarization decomposition characteristics[J].Acta Oceanologica Sinica,2016,35(9):86-90 |
| 结合极化分解特征的SVM溢油检测研究 |
| Oil spill detection by a support vector machine based on polarization decomposition characteristics |
| 投稿时间：2015-08-24 修订日期：2015-11-02 |
| DOI：10.1007/s13131-016-0935-5 |
| 中文关键词: 溢油 极化SAR 特征谱 熵 反熵 SVM |
| 英文关键词:oil spill polarization synthetic aperture radar characteristic spectrum entropy reflection entropy support vector machine |
| 基金项目: |
| 作者 | 单位 | E-mail | | 邹亚荣 | 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 | | | 石立坚 | 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 | shilj@mail.nsoas.org.cn | | 张胜利 | 北京第二外国语学院, 北京, 100024 | | | 梁超 | 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 | | | 曾韬 | 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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| 中文摘要: |
| 海洋环境对海洋资源的利用有着密切的关系，近年来，海上溢油给海洋环境造成巨大的危害，及早发现溢油对于海洋的对于海洋防灾减灾具有重要的意义。遥感是目前主要的溢油监测手段之一，采用全极化SAR数据，对全极化SAR数据进行处理，提取极化分解参数熵H与反熵A，开展H与A的组合特征谱分析，构建溢油极化特征谱，获得对溢油具有明显表现的极化特征谱，从而基于支持向量机进行溢油的遥感检测，结果表明：基于（1-A）（1-H）、（1-H）A的参数提取，效果较为明显；而基于HA参数的溢油信息提取，效果则相对不佳。两者的结合与H、A的值有密切的关系，一般来说H的值大于0.7时，A的值一般较小，此时运用（1-A）（1-H）、（1-H）A参数进行溢油提取效果明显。不论采用那个结合参数，油井信息都会对溢油信息提取造成一定的虚警，尤其是利用（1-A）（1-H）进行溢油信息提取，基于（1-A）H、（1-H）A参数的虚警相对较小，但图像的噪声较大。运用极化特征谱的SVM进行溢油检测，能有效的检测出溢油信息，且精度高于基于单极化特性的检测方法。 |
| 英文摘要: |
| 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 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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