Abstract：
The work presented in this paper is focused on the largest marine disaster to have occurred in the Indian Ocean due to the breakup of the container tanker ship X-Press Pearl. In order to identify the oil spill and its temporal evolution, a recently proposed damping ratio (DR) index is employed. To derive the DR, a data-driven GMM-EM clustering method optimized by stochastic ordering of the resulting classes in Sentinel 1 SAR time series imagery is proposed. A ship-born oil spill site is essentially considered to consist of three subsites: oil, open sea, and ship. The initial site probability densities were determined by using k-means clustering. In addition to the clustering method, two histogram-based approaches, namely contextual peak thresholding (CPT) and contextual peak ordering (CPO), were also formulated and presented. The improved histogram peak detection methods take into account spatial and contextual dependencies. The similarity of the marginal probability densities of the open sea and the oil classes makes it difficult to quantify the DR values to show the level of dampening. In the study, we show that reasonable class separability to correctly determine the σVV0,seaθ is possible by using GMM clustering. Resulting class separability\'s are also reported using JM and ML distances. The methods tested show the range of derived DR values stays significantly within similar ranges to each other. The outcomes were tested with the ground-based surveys conducted during the disaster for oil spill sites and other chemical compounds. The proposed methods are simple to execute, robust, and fully automated. Further, they do not require masking the oil or the selection of high-confidence water pixels manually.
摘要：
本文介绍的工作重点是由于集装箱油轮X-PressPearl的破裂而在印度洋发生的最大的海洋灾难。为了识别漏油事件及其时间演变,采用了最近提出的阻尼比(DR)指标。要导出DR，提出了一种数据驱动的GMM-EM聚类方法，该方法通过对Sentinel1SAR时间序列图像中的结果类进行随机排序来优化。船舶溢油现场基本上被认为由三个子现场组成：石油，公海,和船。通过使用k均值聚类确定初始站点概率密度。除了聚类方法，两种基于直方图的方法，即上下文峰值阈值(CPT)和上下文峰值排序(CPO)，也被制定和提出。改进的直方图峰值检测方法考虑了空间和上下文依赖性。公海和石油类别的边际概率密度的相似性使得难以量化DR值以显示阻尼水平。在研究中,我们证明了通过使用GMM聚类可以正确确定σVV0，seaθ的合理类别可分性。还使用JM和ML距离报告了产生的类可分性。所测试的方法显示导出的DR值的范围显著保持在彼此相似的范围内。在灾难期间对漏油地点和其他化合物进行的地面调查对结果进行了测试。所提出的方法易于执行，健壮,完全自动化。Further,它们不需要手动掩蔽油或选择高置信度水像素。
