OIL SPILL DETECTION IN CALM OCEAN CONDITIONS: A U-NET MODEL NOVEL SOLUTION

被引：1

作者：

Hammoud, Bilal ^{[1
]}

Maroun, Charbel Bou ^{[2
]}

Moursi, Mohamed ^{[1
]}

Wehn, Norbert ^{[1
]}

机构：

[1] EIT Rheinland Pfalz Tech Univ RPTU, Microelect Syst Design Res Grp EMS, Kaiserslautern, Germany

[2] Lebanese Amer Univ LAU, SoE, Dept Elect & Comp Engn, Byblos, Lebanon

来源：

IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2023年

关键词：

Oil spill; drone; U-net model; radar backscattering; detection; SEGMENTATION;

D O I：

10.1109/IGARSS52108.2023.10281482

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

Oil spills severely damage marine life and coastal environments. To reduce their polluting effect on the ecosystem, it is important to promptly react to potential spills for early detection and monitoring. In this paper, we propose a drone-based solution with a deep-learning U-net model. It processes the radar backscattering dominated by the specular component in calm ocean conditions to detect contaminated sea surfaces with oil spills. Results show that our approach achieves a high detection rate exceeding 90% for thick oil slicks in the range of 1-10 mm.

引用

页码：4658 / 4661

页数：4

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