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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing Vol. 62; pp. 1 - 12
Main Authors: Meng, Tingyu, Nunziata, Ferdinando, Yang, Xiaofeng, Buono, Andrea, Chen, Kun-Shan, Migliaccio, Maurizio
Format: Journal Article
Language:English
Published: New York IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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 Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 equation model augmented with the model of local balance (MLB), an effective dielectric constant model, and a composite medium model to include the effect of an oil slick. Damping ratios (DRs), predicted for different oil parameters (namely, the oil thickness and seawater volume fraction), are used to train and test a four-layer neural network. Once successfully tested, the neural network is applied to an uninhabited aerial vehicle synthetic aperture radar (UAVSAR) image collected during the DeepWater Horizon (DWH) oil spill accident to retrieve the oil slick thickness and volume fraction of seawater in the oil layer. The inversion results show that the thicker (i.e., 2-4 mm) emulsions are located in the south and west of the slick and they are surrounded by thinner (i.e., < 1 mm) oil films. In addition, the seawater volume fraction in the oil slick is found to be about 20%-30%. Results are contrasted with optical data and previous studies of the same accidental oil spill, showing qualitatively good agreement.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2024.3369023