Detecting oil on water using polarimetric imaging

Iler, Amber L.; Hamilton, Patrick D.

doi:10.1117/12.2180169

Cited by 11 publications

(13 citation statements)

References 15 publications

(15 reference statements)

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“…Spreading is one of the most crucial processes that can increase the contamination area (Lee et al, 2015). The spilled oil spreads quickly to form a thin layer (slick) on the water surface due to the balance between several forces (e.g., gravity, inertia forces, surface tension forces, and viscosity forces) (Iler and Hamilton, 2015). Surface and interfacial tensions are important to determine the final thickness of a spreading oil film (Environment Canada, 2013).…”

Section: Spreadingmentioning

confidence: 99%

Diluted Bitumen: Physicochemical Properties, Weathering Processes, Emergency Response, and Recovery

Zhong

Lin

et al. 2022

Front. Environ. Sci.

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Bitumen, an unconventional crude oil, has received much attention with the increasing consumption and the shrinking storage of conventional crude oils. Bitumen is highly viscous and, thus, is commonly diluted for transportation purposes. Spills of diluted bitumen could occur during the transportation from reservoirs to refineries via pipeline, rail, and marine vessels. Although some laboratory and numerical modeling studies have been contributed to study the spill of diluted bitumen from different aspects, there is no systematic review in the field yet. Therefore, this study first conducted a review on different types of diluted bitumen based on their physicochemical properties, followed by their weathering processes including spreading, evaporation, emulsification, photooxidation, biodegradation, and sinking. Second, the numerical modeling on the fate and behavior of spilled diluted bitumen was summarized and analyzed. Finally, the techniques for spilled oil recovery were discussed, as well as the disposal/treatment of oily waste. Currently, a rare attempt has been made to turn the recovered oily waste into wealth (reutilization/valorization of oily waste). Using the recovered oily waste as the feedstock/processing medium for an emerging thermochemical conversion technique (hydrothermal liquefaction of biomass for crude bio-oil production) is highly recommended. Overall, this article summarized the state-of-the-art knowledge of the spill of diluted bitumen, with the hope to create a deep and systematic understanding on the spill of diluted bitumen for researchers, relevant companies, and decision makers.

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Section: Spreadingmentioning

confidence: 99%

Diluted Bitumen: Physicochemical Properties, Weathering Processes, Emergency Response, and Recovery

Zhong

Lin

et al. 2022

Front. Environ. Sci.

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“…A potential method of improving the classification model and applying it with coarser hyperspectral data would be a remote sensing data fusion approach that combines reflectance spectra with polarization imaging. Since the polarization characteristics of oil surface are able to reveal more information about the roughness and texture than using reflectance spectra along [41], they have been applied to the field of oil spill detection [42]- [44]. Therefore, we envision a fusion approach which may be able to distinguish between different types of oil that have similar reflectance spectra, and achieve accurate classification using hyperspectral data with coarser bands.…”

Section: Conclusion and Future Studymentioning

confidence: 99%

Identifying Oil Spill Types Based on Remotely Sensed Reflectance Spectra and Multiple Machine Learning Algorithms

Liu¹,

Yu²,

Xie³

et al. 2021

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

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An accurate identification of oil spill types is the basis of determining the source of leakage, evaluating the potential damage, and deciding a plan of responses for an oil spill event. Despite sufficient studies that interpreted and analyzed hyperspectral data of oil spills, these studies that identify or classify oil spill types is rather limited. Aiming at identifying different types of oil spills, this study analyses the reflectance spectra obtained from high-resolution hyperspectral sensors using multiple machine learning methods. Four types of machine learning models are applied in this study: random forest (RF), support vector machine (SVM), and deep neural network (DNN), and DNN with differential pooling (DP-DNN). The training and testing data are collected by field experiments under different environmental condition in order to verify the robustness of the machine learning models. The characteristics of reflectance is briefly described, and the results conform with results from previous studies. The performances of the machine learning models are evaluated and compared in terms of both accuracy of prediction and computational complexity. The results indicate that the two DNN models are able to achieve the most accurate prediction among the four machine learning models at the cost of more computation. The SVM model, or the proposed DP-DNN model may be a favorable choice when training time is limited.

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“…Oil on water has a polarizing effect on light, so viewing oiled water with polarized lenses can improve contrast and thus oil detection [ 6 , 7 ]. Light reflected from a water surface reflects at 53 degrees (Brewster angle), so one can improve contrast by setting the detector at this Brewster angle (53 degrees from the vertical) [ 8 ].…”

Section: Detection and Mapping Of Oil On A Water Surfacementioning

confidence: 99%

A Review of Oil Spill Remote Sensing

Fingas¹,

Brown

2017

Sensors

268

171

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The technical aspects of oil spill remote sensing are examined and the practical uses and drawbacks of each technology are given with a focus on unfolding technology. The use of visible techniques is ubiquitous, but limited to certain observational conditions and simple applications. Infrared cameras offer some potential as oil spill sensors but have several limitations. Both techniques, although limited in capability, are widely used because of their increasing economy. The laser fluorosensor uniquely detects oil on substrates that include shoreline, water, soil, plants, ice, and snow. New commercial units have come out in the last few years. Radar detects calm areas on water and thus oil on water, because oil will reduce capillary waves on a water surface given moderate winds. Radar provides a unique option for wide area surveillance, all day or night and rainy/cloudy weather. Satellite-carried radars with their frequent overpass and high spatial resolution make these day–night and all-weather sensors essential for delineating both large spills and monitoring ship and platform oil discharges. Most strategic oil spill mapping is now being carried out using radar. Slick thickness measurements have been sought for many years. The operative technique at this time is the passive microwave. New techniques for calibration and verification have made these instruments more reliable.

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Detecting oil on water using polarimetric imaging

Cited by 11 publications

References 15 publications

Diluted Bitumen: Physicochemical Properties, Weathering Processes, Emergency Response, and Recovery

Diluted Bitumen: Physicochemical Properties, Weathering Processes, Emergency Response, and Recovery

Identifying Oil Spill Types Based on Remotely Sensed Reflectance Spectra and Multiple Machine Learning Algorithms

A Review of Oil Spill Remote Sensing

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