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.
Keywords: Contextual peak ordering (CPO); Contextual peak thresholding (CPT); Damping ratio (DR); GMM; K-means; Oil spill remote sensing; Ship wreck oil spills; Synthetic aperture radar (SAR).
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