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 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.

中文翻译：

---

使用 GMM 聚类和阻尼比对印度洋 X-Press Pearl 船灾难进行数据驱动的溢油测绘

本文介绍的工作重点是因集装箱油轮 X-Press Pearl 解体而在印度洋发生的最大海洋灾难。为了识别漏油及其时间演变，采用了最近提出的阻尼比（DR）指数。为了导出 DR，提出了一种数据驱动的 GMM-EM 聚类方法，该方法通过对 Sentinel 1 SAR 时间序列图像中的结果类进行随机排序来优化。船载溢油现场基本上被认为由三个子现场组成：石油、公海和船舶。初始位点概率密度是通过使用 k 均值聚类来确定的。除了聚类方法之外，还制定并提出了两种基于直方图的方法，即上下文峰值阈值处理（CPT）和上下文峰值排序（CPO）。改进的直方图峰值检测方法考虑了空间和上下文依赖性。公海和油类的边际概率密度的相似性使得量化 DR 值以显示阻尼水平变得困难。在这项研究中，我们表明通过使用 GMM 聚类，正确确定合理的类可分离性是可能的。还使用 JM 和 ML 距离报告所得的类可分离性。测试的方法显示得出的 DR 值的范围明显保持在彼此相似的范围内。结果通过灾难期间对石油泄漏地点和其他化合物进行的地面调查进行了测试。所提出的方法执行简单、稳健且完全自动化。此外，它们不需要手动遮盖油或选择高置信度水像素。