School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Iran.
School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Iran; Centre Eau Terre Environnement, Institut National de la Recherche Scientifique, Quebec City, Canada.
Mar Pollut Bull. 2023 May;190:114834. doi: 10.1016/j.marpolbul.2023.114834. Epub 2023 Mar 17.
Oil spills are the main threats to marine and coastal environments. Due to the increase in the marine transportation and shipping industry, oil spills have increased in recent years. Moreover, the rapid spread of oil spills in open waters seriously affects the fragile marine ecosystem and creates environmental concerns. Effective monitoring, quick identification, and estimation of the volume of oil spills are the first and most crucial steps for a successful cleanup operation and crisis management. Remote Sensing observations, especially from Synthetic Aperture Radar (SAR) sensors, are a very suitable choice for this purpose due to their ability to collect data regardless of the weather and illumination conditions and over far and large areas of the Earth. Owing to the relatively complex nature of SAR observations, machine learning (ML) based algorithms play an important role in accurately detecting and monitoring oil spills and can significantly help experts in faster and more accurate detection. This paper uses SAR images from ESA's Copernicus Sentinel-1 satellite to detect and locate oil spills in open waters under different environmental conditions. To this end, a deep learning framework has been presented to identify oil spills automatically. The SAR images were segmented into two classes, the oil slick and the background, using convolutional neural networks (CNN) and vision transformers (ViT). Various scenarios for the proposed architecture were designed by placing ViT networks in different parts of the CNN backbone. An extensive dataset of oil spill events in various regions across the globe was used to train and assess the performance of the proposed framework. After the detection performance assessments, the F1-score values for the standard DeepLabV3+, FC-DenseNet, and U-Net networks were 75.08 %, 73.94 %, and 60.85, respectively. In the combined networks models (combination of CNN and ViT), the best F1-score results were obtained as 78.48 %. Our results showed that these hybrid models could improve detection accuracy and have a high ability to distinguish oil spill borders even in noisy images. Evaluation metrics are increased in all the combined networks compared to the original CNN networks.
溢油是海洋和沿海环境的主要威胁。由于海洋运输和航运业的增加，近年来溢油事故有所增加。此外，溢油在开阔水域的迅速扩散严重影响了脆弱的海洋生态系统，并引发了环境问题。有效的监测、快速识别和估算溢油量是成功清理作业和危机管理的首要和最关键的步骤。遥感观测，特别是合成孔径雷达 (SAR) 传感器的观测，由于其能够在无论天气和光照条件下，以及在地球的广大和遥远地区收集数据，是一种非常合适的选择。由于 SAR 观测具有相对复杂的性质，基于机器学习 (ML) 的算法在准确检测和监测溢油方面发挥着重要作用，并可以显著帮助专家更快、更准确地检测。本文使用欧洲航天局哥白尼哨兵-1卫星的 SAR 图像，在不同的环境条件下检测和定位开阔水域中的溢油。为此，提出了一种深度学习框架来自动识别溢油。使用卷积神经网络 (CNN) 和视觉转换器 (ViT) 将 SAR 图像分为油膜和背景两类。通过在 CNN 骨干网的不同部分放置 ViT 网络，设计了各种用于提出的架构的场景。使用来自全球不同地区的各种溢油事件的广泛数据集来训练和评估所提出框架的性能。在检测性能评估后，标准 DeepLabV3+、FC-DenseNet 和 U-Net 网络的 F1 分数值分别为 75.08%、73.94%和 60.85%。在组合网络模型（CNN 和 ViT 的组合）中，获得了最佳的 F1 分数结果为 78.48%。我们的结果表明，这些混合模型可以提高检测精度，并且即使在嘈杂的图像中也具有区分溢油边界的高能力。与原始 CNN 网络相比，所有组合网络的评估指标都有所提高。
