A review on marine radar oil spill monitoring technology

XU Jin; LI Bo; CHEN Rong; MA Long; ZHAO Zhiqiang; LI Xidong

doi:10.12111/j.mes.2022-x-0306

Volume 42 Issue 5

Oct. 2023

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Chinese Journal of MARINE ENVIRONMENTAL SCIENCE > 2023 > 42(5): 805-812. > DOI: 10.12111/j.mes.2022-x-0306

XU Jin, LI Bo, CHEN Rong, MA Long, ZHAO Zhiqiang, LI Xidong. A review on marine radar oil spill monitoring technology[J]. Chinese Journal of MARINE ENVIRONMENTAL SCIENCE, 2023, 42(5): 805-812. DOI: 10.12111/j.mes.2022-x-0306

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A review on marine radar oil spill monitoring technology

XU Jin^{1, 2, ,},
LI Bo^1,,
CHEN Rong¹,
MA Long^{1, 2},
ZHAO Zhiqiang^{1, 2},
LI Xidong¹

1.
Naval Architecture and Shipping College, Guangdong Ocean University, Zhanjiang 524088, China
2.
Shenzhen Institute of Guangdong Ocean University, Shenzhen 518120, China

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Received Date: November 07, 2022
Revised Date: February 14, 2023
Accepted Date: February 14, 2023
Available Online: September 25, 2023

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Abstract

Abstract

Oil spill ranks first in terms of occurrence frequency, pollution degree and long-term negative effects in marine environmental pollution. To minimize the losses after the oil spill accident, advanced technology for monitoring the oil spill in real-time is an important guarantee for emergency treatment and rapid recovery of marine ecological environment. The marine radar oil spill monitoring technology has the advantage of assisting emergency response all-day and all-weather. It has gradually attracted attention in the past decade. The marine radar oil spill monitoring technology is summarized and analyzed from the aspects of data preprocessing, effective region extraction, oil film segmentation. The oil spill identification method based on image texture features, machine learning and adaptive threshold is the mainstream in marine radar oil spill monitoring technology. With the continuous accumulation of the image samples, marine radar oil spill semantic segmentation using deep learning is the main direction in the future.
- marine radar,
- oil spill,
- machine learning,
- texture feature

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