Scattering Model-Based Oil-Slick-Related Parameters Estimation From Radar Remote Sensing: Feasibility and Simulation Results

In this study, the potential of electromagnetic scattering models to retrieve quantitative parameters of sea oil spills is investigated using an artificial intelligence (AI)-based approach. The backscattering coefficient of a slick-covered sea surface is predicted using the advanced integral equatio...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing Jg. 62; S. 1 - 12
Hauptverfasser:	Meng, Tingyu, Nunziata, Ferdinando, Yang, Xiaofeng, Buono, Andrea, Chen, Kun-Shan, Migliaccio, Maurizio
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Artificial intelligence Artificial neural network (ANN) Damping Damping ratio Deep water DeepWater Horizon (DWH) Dielectric constant Electromagnetic scattering Emulsions Integral equations Mathematical models Neural networks Oil slicks oil spill Oil spills Oils Parameter estimation parameter inversion Parameters Predictive models Radar Radar imaging Remote sensing Rough surfaces SAR (radar) Scattering scattering model Sea surface Seawater Surface roughness Surface waves Synthetic aperture radar synthetic aperture radar (SAR) Thickness
ISSN:	0196-2892, 1558-0644
Online-Zugang:	Volltext
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