| 一种基于经验模态分解的SAR图像溢油识别算法 |
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| 引用本文： | 杨勇虎,李颖,朱雪媛.一种基于经验模态分解的SAR图像溢油识别算法[J].海洋学报(英文版),2017,36(7):86-94. |
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| 作者姓名： | 杨勇虎 李颖 朱雪媛 |
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| 作者单位： | 大连海事大学, 航海学院, 大连, 116026;大连东软信息学院, 计算机科学与技术系, 大连, 116023,大连海事大学, 航海学院, 大连, 116026,大连海事大学, 航海学院, 大连, 116026 |
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| 基金项目： | The National Science and Technology Support Project under contract No. 2014BAB12B02; the Natural Science Foundation of Liaoning Province under contract No. 201602042. |
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| 摘 要： | 溢油事件的发生会给海洋环境的保护和经济发展带来巨大的影响。运用现代化的监测手段和技术进行监测，及时发现溢油现象和违规行为，保护海洋环境是非常重要的。合成孔径雷达（SAR）技术是溢油检测的有效工具，在SAR图像中溢油表现为黑色的区域，但是黑色区域也可能会由其他的因素引起。本文提出了一种基于二维经验模态分解（BEMD）的方法来识别溢油和疑似溢油。首先通过BEMD方法将感兴趣的区域分解为局部窄带的各分量—内蕴模函数（BIMF）之和，并对分解后得到的各分量IMF进行Hilbert变换，通过Hibert谱分析得到64维的特征空间，然后使用Relief方法得到5个特征向量，最后利用马氏距离分类器进行分类。通过实验结果表明，该方法能够有效、准确地检测出溢油，准确率超过90%。
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| 关 键 词： | 二维经验模态分解 合成孔径雷达图像 溢油探测 希伯特谱分析 |
| 收稿时间： | 2016/6/15 0:00:00 |
| 修稿时间： | 2016/7/5 0:00:00 |
A novel oil spill detection method from synthetic aperture radar imageries via a bidimensional empirical mode decomposition |
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| YANG Yonghu,LI Ying and ZHU Xueyuan.A novel oil spill detection method from synthetic aperture radar imageries via a bidimensional empirical mode decomposition[J].Acta Oceanologica Sinica,2017,36(7):86-94. |
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| Authors: | YANG Yonghu LI Ying and ZHU Xueyuan |
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| Institution: | 1.Navigation College, Dalian Maritime University, Dalian 116026, China;Computer Science and Technology Department, Dalian Neusoft Institute of Information, Dalian 116023, China2.Navigation College, Dalian Maritime University, Dalian 116026, China |
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| Abstract: | Oil spills pose a major threat to ocean ecosystems and their health. Synthetic aperture radar (SAR) sensors can detect oil spills on the sea surface. These oil spills appear as dark spots in SAR images. However, dark formations can be caused by a number of phenomena. It is aimed to distinguishing oil spills or look-alike objects. A novel method based on a bidimensional empirical mode decomposition is proposed. The selected dark formations are first decomposed into several bidimensional intrinsic mode functions and the residue. Subsequently, 64 dimension feature sets are calculated using the Hilbert spectral analysis and five new features are extracted with a relief algorithm. Mahalanobis distances are then used for classification. Three data sets containing oil spills or look-alikes are used to test the accuracy rate of the method. The accuracy rate is more than 90%. The experimental results demonstrate that the novel method can detect oil spills validly and accurately. |
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| Keywords: | bidimensional empirical mode decomposition synthetic aperture radar image detection of oil spill hilbert spectral analysis |
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