Electrical and Computer Engineering, University of South Alabama, Mobile, AL 36688, USA.
College of Engineering, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates.
Sensors (Basel). 2021 Mar 28;21(7):2351. doi: 10.3390/s21072351.
Oil leaks onto water surfaces from big tankers, ships, and pipeline cracks cause considerable damage and harm to the marine environment. Synthetic Aperture Radar (SAR) images provide an approximate representation for target scenes, including sea and land surfaces, ships, oil spills, and look-alikes. Detection and segmentation of oil spills from SAR images are crucial to aid in leak cleanups and protecting the environment. This paper introduces a two-stage deep-learning framework for the identification of oil spill occurrences based on a highly unbalanced dataset. The first stage classifies patches based on the percentage of oil spill pixels using a novel 23-layer Convolutional Neural Network. In contrast, the second stage performs semantic segmentation using a five-stage U-Net structure. The generalized Dice loss is minimized to account for the reduced oil spill representation in the patches. The results of this study are very promising and provide a comparable improved precision and Dice score compared to related work.
油轮、船舶和管道裂缝将油泄漏到水面上，对海洋环境造成相当大的破坏和危害。合成孔径雷达 (SAR) 图像为目标场景提供了近似表示，包括海洋和陆地表面、船舶、溢油和类似物。从 SAR 图像中检测和分割溢油对于泄漏清理和保护环境至关重要。本文提出了一种基于高度不平衡数据集的两阶段深度学习框架，用于识别溢油事件。第一阶段使用一种新颖的 23 层卷积神经网络根据溢油像素的百分比对斑块进行分类。相比之下，第二阶段使用五阶段 U-Net 结构进行语义分割。广义的 Dice 损失最小化，以考虑斑块中减少的溢油表示。这项研究的结果非常有前景，与相关工作相比，提供了可比的精度和 Dice 得分的提高。
