Sunken oil detection and classification using MBES backscatter data.
Sunken oil detection and classification using MBES backscatter data.
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DOI：
10.1016/j.marpolbul.2022.113795
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发表时间：
2022-06
影响因子：
5.8
通讯作者：
Jianwei Li;Wei An;Chao Xu;Jun Hu;Huiwang Gao;Weidong Du;Xueyan Li
中科院分区：
文献类型：
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作者：
Jianwei Li;Wei An;Chao Xu;Jun Hu;Huiwang Gao;Weidong Du;Xueyan Li
近十年来，渤海海域多次发生沉油事件。目前，快速有效地对沉油进行检测和分类仍然是一个难题。在这项研究中，声纳探测实验进行，以获得声学图像样本，使用多波束回声测深仪（MBES）在一个大型的海水储罐底部的区域，沉没的石油位于。构造了一系列MBES数据校正，以生成能够直接反映目标特征的后向散射强度图像。同时，提取八维特征，建立支持向量机（SVM）分类框架，对沉油和其他干扰目标进行分类。结果表明，MBES后向散射图像提供了一种可供选择的方法来检测和分类沉没的石油。SVM算法的总体目标分类准确率达到88.5%。该研究为进一步开展沉油探测研究提供了依据。
Sunken oil incidents have occurred multiple times in the Bohai Sea over the past ten years. Currently, quick and effective sunken oil detection and classification remains a difficult problem. In this study, sonar detection experiments are conducted to obtain acoustic image samples using a multibeam echosounder (MBES) in a large seawater tank at the bottom of the area where the sunken oil is located. A series of MBES data corrections are constructed to generate backscatter strength images that can reflect the target characteristics directly. Meanwhile, eight-dimensional features are extracted, and a support vector machine (SVM) classification framework is built to classify the sunken oil and other interference targets. The results indicate that the MBES backscatter images provide an alternative approach for detecting and classifying sunken oil. The overall target classification accuracy reaches 88.5% by the SVM algorithm. Thus, this study provides a basis for further investigation of detecting sunken oil.