In this study, a model-based neural network approach is proposed to retrieve ancillary parameters related to oil pollutants at sea. The proposed methodology consists of two pillars. First, an electromagnetic scattering model is used to generate radar backscatter for slick-free and slick-covered sea surface at variance of incidence angle, faction of water into the oil and oil thickness. Then, these radar backscatter values are combined to generated a metric adopted fro the retrieval process, namely the damping ratio. Second, an artificial neural network is first trained on the simulated damping ratio DR and then applied to actual synthetic aperture radar imagery to retrieve oil thickness and seawater volume fraction. Results, obtained processing synthetic aperture radar scenes collected during the Deep Water Horizon oil accident by the L-band uninhabited aerial vehicle synthetic aperture radar (National Aeronautics and Space Administration - Jet Propulsion Laboratory), show the soundness of the proposed methodology.

The registration and mapping of The Miniature Radio Frequency (Mini-RF) images from The Lunar Reconnaissance Orbiter (LRO) and the derived data products has been a problem for lunar remote sensing analysis and multi-source data fusion. In this context, we propose an automated registration methodology for Mini-RF S-band level-1 data. This method corrects offsets from synthetic aperture radar (SAR) imaging to match SAR data with optical and DEM data in the map-projection. It could produce maps in polar and non-polar area with precision comparable to manually registered maps. Using manually-labeled craters features for evaluation, the processed maps match well to LRO Wide Angle Camera (WAC) and Digital Elevation Model (DEM) data on 100m- level.

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.

In this study, the Artificial Neural Network technique is used to retrieve quantitative parameters of marine oil spill on Synthetic Aperture Radar imagery. In fact, while Synthetic Aperture Radar has been widely exploited to obtain morphological features of sea oil spills as extent and shape, its potential to extract oil thickness information has been underexplored. Hence, an artificial neural network is proposed that have been trained and tested using the damping ratio predicted by a microwave scattering model consisting of Advanced Integral Equation Method in combination with the local balance damping model and a layered-medium dielectric model. Experiments are performed on a L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar image collected during the DeepWater Horizon oil spill accident. The inversion results show that the central area of the slick is the thicker part of the oil emulsion (2 – 4 mm thickness), surrounded by thinner oil film whose thickness is lower than 1 mm.

In this study, the bistatic radar scattering coefficients related to an oil-covered sea surface are predicted by modeling both the oil damping effect on surface roughness–through the advanced integral equation method–and the oil modification on the dielectric properties of the scattering surface. The bistatic scattering is analyzed in the whole upper scattering space under different radar frequencies, incidence angles, wind speeds, and oil thicknesses. Numerical predictions show that the scattering energy of an oil-covered sea surface is generally higher in the forward scattering zone than that in the backward one. In addition, the oil damping effect is the main mechanism ruling the scattering behavior in the backward region. The information related to the bistatic scattering geometry is also explored to retrieve oil thickness, representing one of the key parameters for radar-based marine oil pollution observation. A new index is proposed to quantify the sensitivity of bistatic scattering coefficients to oil thickness in different cases: single-polarization features, dual co-polarization features, namely, the polarization ratio (PR) and the normalized polarization difference index (NPDI), and dual-angular scattering features. Numerical results show that the bistatic scattering coefficients result in an enhanced sensitivity to oil thickness with respect to the monostatic case. The single HH-polarized scattering coefficients show better oil thickness sensitivity in the backward region, while the VV-polarized ones are more sensitive to oil thickness in the forward region. The combination of dual-polarized scattering coefficients significantly improves the oil thickness sensitivity compared to single-polarization radar observations, especially in the forward region. PR outperforms NPDI, even though the latter can suppress the effect of wind speed. The combination of dual-angular observations can significantly increase its sensitivity of oil thickness in the backward region but at the expense of reduced sensitivity in the forward region.

Oils floating on the sea surface can be observed as “dark” patches on radar images since the backscattered signals from the contaminated area are reduced in two dominant ways. First, oil slicks could damp short gravity and capillary waves on the sea surface responsible for backscattering energy. Second, the oil-covered sea surface permittivity decreases significantly if the oil film is sufficiently thick or mixed with seawater, i.e., oil emulsion. In this article, the geometry of the oil-covered sea surface is accounted for by the damping of sea waves, which is described by the model of local balance (MLB) combined with the sea wave spectrum. The radar backscattering is predicted by the advanced integral equation method (AIEM) model. The reflection coefficients are calculated based on a layered-medium model to analyze the impact of oil thickness and emulsions on the radar scattering. Numerical simulations demonstrate that: 1) the sensitivity to oil thickness and water content of the oil spills increases when the radar frequency increases; 2) the backscattering signals exhibit a nonlinear behavior with respect to oil thickness; and 3) high wind speed can generally narrow the difference between the radar backscattering from the clean and oil-covered sea surface, while the incidence angle has little effect. Numerical simulations are then compared with the multifrequency synthetic aperture radar observations acquired during the Gulf of Mexico Deepwater Horizon (DWH) oil spill accident and the 2011 Norwegian Clean Seas Association for Operating Companies (NOFO) oil-on-water exercise. Comparison results show that it is possible to estimate the oil thickness at reasonably good accuracy.

Emulsified oil slicks can not only damp short gravity and capillary waves on the sea surface, but also reduce the permittivity of the contaminated area. This paper simulates the backscattering coefficients of emulsion oil covered sea surfaces based on AIEM, with the damping model described by model of local balance (MLB). The sea surface covered by emulsion oil with finite thickness is modeled as a layered-medium to calculate the composite reflection coefficients. The simulation results of oil-covered sea surface are compared to those of clean sea surfaces and discussed in terms of incidence angles and frequencies of EM waves, oil thickness and wind speeds.