Automatic detection of oil spills from SAR satellite data using genetic algorithm

Marghany, Maged

doi:10.1016/b978-0-12-818111-9.00011-2

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…SAR remote sensing images can be analyzed through model algorithms, including neural network models, oil drift models, genetic algorithms, machine learning algorithms, deep learning methods, etc. [34][35][36][37][38]. However, electromagnetic wave has a large signal attenuation coefficient when transmitting in seawater, requiring a lot of energy to support transmission.…”

Section: Traditional Oil Spill Monitoring Methodsmentioning

confidence: 99%

Underwater Acoustic Technology-Based Monitoring of Oil Spill: A Review

Pan

Tang

Jiang

et al. 2023

JMSE

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Acoustic monitoring is an efficient technique for oil spill detection, and the development of acoustic technology is conducive to achieving real-time monitoring of underwater oil spills, providing data references and guidance for emergency response work. Starting from the research background of oil spills, this review summarizes and evaluates the existing research on acoustic technology for monitoring underwater oil spills. Underwater oil spills are more complex than surface oil spills, and further research is needed to investigate the feasibility of acoustic technology in underwater oil spill monitoring, verify the accuracy of monitoring data, and assess its value. In the future, the impact mechanism and dynamic research of acoustic technology in oil spill monitoring should be explored, and the advantages and differences between acoustic technology and other detection techniques should be compared. The significance of auxiliary mechanisms combined with acoustic technology in oil spill monitoring should be studied. Moreover, acoustic research methods and experimental techniques should be enriched and improved to fully tap into the future value of acoustic technology.

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