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基于支持向量机的改进语义分割模型在 SAR 图像海洋溢油检测中的应用。

An improved semantic segmentation model based on SVM for marine oil spill detection using SAR image.

机构信息

College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China; Technology Innovation Center for Maritime Silk Road Marine Resources and Environment Networked Observation, Ministry of Natural Resources, Qingdao 266580, China.

Qingdao Geo-Engineering Surveying Institute (Qingdao Geological Exploration Development Bureau), Qingdao 266071, China.

出版信息

Mar Pollut Bull. 2023 Jul;192:114981. doi: 10.1016/j.marpolbul.2023.114981. Epub 2023 May 18.

Abstract

In the oil industry, oil spills occur due to offshore rig explosions, ship collisions, and other reasons. It is crucial to accurately and rapidly identify oil spills to protect marine ecosystems. Synthetic aperture radar (SAR) can all-weather and all-time work and provide a wealth of polarization information for identification of oil spills based on semantic segmentation model. However, the performance of classifiers in the semantic segmentation model has become a significant challenge to improving recognition ability. To solve this problem, an improved semantic segmentation model named DRSNet was proposed, which uses ResNet-50 as the backbone in DeepLabv3+ and support vector machines (SVM) as the classifier. The experiment was conducted using ten polarimetric features from SAR images and results demonstrate that the DRSNet performs best compared to other semantic segmentation models. Current work provides a valuable tool to enhance maritime emergency management capabilities.

摘要

在石油行业,由于海上钻井平台爆炸、船只碰撞等原因,会发生石油泄漏。准确、快速地识别石油泄漏对于保护海洋生态系统至关重要。合成孔径雷达 (SAR) 可以全天候、全时段工作,并根据语义分割模型为识别石油泄漏提供丰富的极化信息。然而,语义分割模型中的分类器的性能已成为提高识别能力的重大挑战。为了解决这个问题,提出了一种名为 DRSNet 的改进语义分割模型,它在 DeepLabv3+ 中使用 ResNet-50 作为骨干,并使用支持向量机 (SVM) 作为分类器。该实验使用 SAR 图像的十个极化特征进行,结果表明,与其他语义分割模型相比,DRSNet 的性能最佳。目前的工作为增强海上应急管理能力提供了一种有价值的工具。

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