Artificial Neural Networks-Based Radar Remote Sensing to Estimate Geographical Information during Oil-Spills

Hammoud, Bilal; Maroun, Charbel Bou; Ney, Jonas; Wehn, Norbert

doi:10.23919/eusipco55093.2022.9909727

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Cited by 2 publications

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DATE on the Edge: On-Site Oil Spill Detection and Thickness Estimation Using Drone-Based Radar Backscattering

Hammoud,

Maroun,

Wehn

2025

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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DATE on the Edge: On-Site Oil Spill Detection and Thickness Estimation Using Drone-Based Radar Backscattering

Hammoud,

Maroun,

Wehn

2025

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Recent Advances in Oil-Spill Monitoring Using Drone-Based Radar Remote Sensing

Hammoud¹,

Wehn²

2023

Environmental Sciences

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Oil spills are regrettably common and have socioeconomic implications on communities and disastrous consequences on the marine ecosystem and maritime life. The European Space Agency (ESA) has stated that worldwide spillage exceeds 4.5 million tons of oil annually, where 45% of the amount is due to operative discharges from ships. To alleviate the severity of oil spills and promptly react to such incidents, it is crucial to have oil-spill monitoring systems, which enable an effective contingency plan to dictate the best actions for dealing with oil spills. A quick and efficient intervention requires the (1) detection of oil slicks, (2) thickness estimation, and (3) oil classification. The European Maritime Safety Agency (EMSA) highlighted in 2016 the need to use drones as complementary systems supporting satellite maritime surveillance. While multiple sensors could be used, active radars appear to be prominent for oil spill monitoring. In this chapter, we present recent advances in drone-based radar remote sensing as an effective oil spill monitoring system. It shows from the system-level perspective the capability of radar systems on drones, using high spectral resolution and parallel scanning, to perform the above-required functionalities (1, 2, and 3) and provide valuable information to contain the damage.

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Artificial Neural Networks-Based Radar Remote Sensing to Estimate Geographical Information during Oil-Spills

Cited by 2 publications

References 18 publications

DATE on the Edge: On-Site Oil Spill Detection and Thickness Estimation Using Drone-Based Radar Backscattering

DATE on the Edge: On-Site Oil Spill Detection and Thickness Estimation Using Drone-Based Radar Backscattering

Recent Advances in Oil-Spill Monitoring Using Drone-Based Radar Remote Sensing

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