This paper presents the utilization of Synthetic Aperture Radar (SAR) data for monitoring and detection of oil spills. In this work, a case study of an oil spill has been investigated using C-band Sentinel-1A SAR data to detect the oil spill that occurred on 28 January 2017, near Ennore port, Chennai, India. Oil spill damages marine ecosystems causing serious environmental effects. Quite often, oil spills on the sea/ocean surface are seen nowadays, mainly in major shipping routes. They are caused due to tanker collisions, illegal discharge from the ships, etc. An oil spill can be monitored and detected using various platforms such as vessel-based, airborne-based and satellite-based. Vessel based and airborne methods are expensive with less area coverage. This process also consumes more time. For ocean applications such as oil spill and Ship detection, optical sensors cannot image during bad weather. As SAR is an active sensor, weather independent, and has cloud penetrating capability, the images can be acquired during the day as well as at night. Radar Remote Sensing (RRS) has rapidly gained popularity for monitoring and detection of oil spills and ships for more than a decade. With the availability of the satellite images, detection of oil spill has improved due to its wide coverage and less revisit time. The present paper gives an overview of the methodologies used to detect oil spills on the SAR images using dual-pol Sentinel-1A Level 1 SLC data. This work clearly demonstrates the preprocessing steps of the Sentinel 1A data for oil spill detection. The oil spill was only visible in the VV channel, therefore, for ocean application VV channel image is preferred. SEASAT was the first space-borne SAR mission launched in 1978 by NASA to observe sea surface. The preprocessing was carried out at the European Space Agency (ESA), the Sentinel Application Platform (SNAP) toolbox and Envi 5.1 toolbox. Based on the Sigma naught values, oil spill can be discriminated with the ocean surface. The results obtained with the VV channel are satisfactory and one could map out the oil spill very well. Supervised classifiers SVM and NN were applied on the boxcar filtered 3 × 3 VV channel image to delineate the oil spill. The result of oil spill detection mapping is validated with Supervised SVM and Neural Network classifiers. The results show there is a good agreement between oil spill mapping and classified image using SVM and NN classified images. The Overall Accuracy (OA) obtained using SVM classifier is 98.13% with kappa coefficient as 0.95 and using NN classifier is 98.11% with kappa coefficients 0.95. This technique is considered to be a potential proxy for the detection and monitoring of Oil spills on water bodies. Application of SAR data for oil spill detection is considered to be first of its kind from Indian coasts. This study aims to detect the oil spill occurred due to collision of two LPG tankers with Sentinel-1A SLC data in Chennai coast area.

本文介绍了利用合成孔径雷达 (SAR) 数据监测和检测漏油情况。在这项工作中，使用 C 波段 Sentinel-1A SAR 数据调查了一个漏油案例研究，以检测 2017 年 1 月 28 日在印度钦奈恩诺尔港附近发生的漏油事件。石油泄漏破坏海洋生态系统，造成严重的环境影响。现在经常看到海面/海洋表面的石油泄漏，主要发生在主要航线上。它们是由于油轮碰撞、船舶非法排放等引起的。可以使用各种平台（如船基、机载和卫星）监测和检测漏油。基于船舶和机载的方法价格昂贵，覆盖面积较小。这个过程也消耗更多的时间。对于漏油和船舶检测等海洋应用，光学传感器在恶劣天气下无法成像。由于 SAR 是一种主动传感器，不受天气影响，并且具有穿透云的能力，因此可以在白天和晚上获取图像。十多年来，雷达遥感 (RRS) 在监测和检测石油泄漏和船舶方面迅速普及。随着卫星图像的可用性，漏油检测由于其覆盖范围广和重访时间较短而得到改善。本文概述了使用双极化 Sentinel-1A 1 级 SLC 数据检测 SAR 图像上的漏油的方法。这项工作清楚地展示了用于溢油检测的 Sentinel 1A 数据的预处理步骤。溢油仅在 VV 通道中可见，因此，对于海洋应用，首选 VV 通道图像。SEASAT 是 NASA 于 1978 年发射的第一个用于观测海面的星载 SAR 任务。预处理是在欧洲航天局 (ESA)、哨兵应用平台 (SNAP) 工具箱和 Envi 5.1 工具箱中进行的。根据西格玛零值，可以区分漏油与海洋表面。使用 VV 通道获得的结果是令人满意的，可以很好地绘制出漏油图。有监督的分类器 SVM 和 NN 应用于经过过滤的 3 × 3 VV 通道图像以描绘漏油。溢油检测映射的结果通过监督 SVM 和神经网络分类器进行验证。结果表明，溢油映射与使用 SVM 和 NN 分类图像的分类图像之间有很好的一致性。使用 SVM 分类器获得的总体准确度 (OA) 为 98.13%，kappa 系数为 0.95，使用 NN 分类器为 98.11%，kappa 系数为 0.95。该技术被认为是检测和监测水体溢油的潜在替代方法。SAR 数据在溢油检测中的应用被认为是印度海岸的首创。本研究旨在检测金奈海岸地区两艘液化石油气油轮与 Sentinel-1A SLC 数据相撞而发生的漏油事件。SAR 数据在溢油检测中的应用被认为是印度海岸的首创。本研究旨在检测金奈海岸地区两艘液化石油气油轮与 Sentinel-1A SLC 数据相撞而发生的漏油事件。SAR 数据在溢油检测中的应用被认为是印度海岸的首创。本研究旨在检测金奈海岸地区两艘液化石油气油轮与 Sentinel-1A SLC 数据相撞而发生的漏油事件。