Infrared polarimetric sensing of oil on water

Chenault, David B.; Vaden, Justin; Mitchell, Douglas A.; Demicco, Erik D.

doi:10.1117/12.2241866

Cited by 15 publications

(10 citation statements)

References 1 publication

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“…Oil of thicknesses greater than about 10 μm, absorbs light in the visible region and re-radiates a portion of this in the infrared spectrum, mostly in the 8-14 μm wavelengths [12][13][14]. Solar-heated oil In summary, the visible spectrum remains a research area as well as an economical method for monitoring oil spills, particularly on countermeasures support operations.…”

Section: Use Of the Infrared (Ir)mentioning

confidence: 99%

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Review of oil spill remote sensing

Fingas

Brown

2014

Marine Pollution Bulletin

466

213

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Section: Use Of the Infrared (Ir)mentioning

confidence: 99%

“…Oil of thicknesses greater than about 10 µm, absorbs light in the visible region and re-radiates a portion of this in the infrared spectrum, mostly in the 8-14 µm wavelengths [12][13][14]. Solar-heated oil will emit infrared radiation as oil shows greater infrared emissivity than water.…”

Section: Use Of the Infrared (Ir)mentioning

confidence: 99%

Review of oil spill remote sensing

Fingas

Brown

2014

Marine Pollution Bulletin

466

213

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“…Polarization of light provides valuable information about scenes that cannot be obtained directly from intensity or spectral images and polarization images seek to obtain the vector nature of the optical field from the scene [1]. Polarization imaging is widely used in those fields, such as industrial inspection [2], environmental monitoring [3], biomedical diagnosis [4,5], marine detection [6][7][8], remote sensing [9][10][11], military reconnaissance [12][13][14], etc.…”

Section: Introductionmentioning

confidence: 99%

An Image Auxiliary Method for Quantitative Analysis of Laser-Induced Breakdown Spectroscopy

Zhang

Sun

et al. 2018

Anal. Chem.

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Improving both the stability and accuracy of laser-induced breakdown spectroscopy (LIBS) is an issue in quantitative analysis. For certain environments outside of the laboratory, consistently and exactly maintaining the distance from the optical system to the sample surface is difficult, and fluctuations of this distance severely affect the stability of the spectrum. In this work, the principal components of the plasma images are extracted and used to correct the spectral line intensities as an auxiliary method to reduce spectral fluctuation. The presented image auxiliary method is combined with univariate analysis and multivariate analysis, and the element concentrations of Cu, Mn, V, and Cr in steel samples are analyzed. For univariate analysis, all the determination coefficients ( R) of the four elements exceed 0.99, whereas the average relative standard deviations (RSDs) of the intensities decrease from 30.45, 23.14, 27.03, and 22.04%, to 2.13, 3.38, 2.49, and 3.58%, respectively. For the multivariate analysis, the R values for Cu, Mn, V, and Cr also all exceed 0.99, and the average RSDs of the predicted concentrations of the validation samples decrease to 2.87, 3.82, 2.86, and 6.51%, respectively.

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“…Because specific research on oil spills in ports with drone technology is sparse, we propose a drone based hybrid RGB/thermal infrared solution. While using thermal infrared imaging, we are able to detect the oil spill on water, because oil will absorb light in the visible region and re-radiates a portion of that light in the thermal infrared spectrum [13,14]. Because oil has a greater infrared emissivity than water, it will appear hot as compared to water in the daytime.…”

mentioning

confidence: 99%

Oil Spill Detection Using Machine Learning and Infrared Images

et al. 2020

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The detection of oil spills in water is a frequently researched area, but most of the research has been based on very large patches of crude oil on offshore areas. We present a novel framework for detecting oil spills inside a port environment, while using unmanned areal vehicles (UAV) and a thermal infrared (IR) camera. This framework is split into a training part and an operational part. In the training part, we present a process for automatically annotating RGB images and matching them with the IR images in order to create a dataset. The infrared imaging camera is crucial to be able to detect oil spills during nighttime. This dataset is then used to train on a convolutional neural network (CNN). Seven different CNN segmentation architectures and eight different feature extractors are tested in order to find the best suited combination for this task. In the operational part, we propose a method to have a real-time, onboard UAV oil spill detection using the pre-trained network and a low power interference device. A controlled experiment in the port of Antwerp showed that we are able to achieve an accuracy of 89% while only using the IR camera.

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Infrared polarimetric sensing of oil on water

Cited by 15 publications

References 1 publication

Review of oil spill remote sensing

Review of oil spill remote sensing

An Image Auxiliary Method for Quantitative Analysis of Laser-Induced Breakdown Spectroscopy

Oil Spill Detection Using Machine Learning and Infrared Images

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