Evaluation of the Ability of Spectral Indices of Hydrocarbons and Seawater for Identifying Oil Slicks Utilizing Hyperspectral Images

Zhao, Dong; Cheng, Xiao; Zhang, Hongping; Niu, Yanfei; Qi, Yangyang; Zhang, Haitao

doi:10.3390/rs10030421

Cited by 17 publications

(9 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Hence, it is possible to combine information in the separate wavelength bands in order to detect oil spills [26]. In [27], it is shown that it is possible to differentiate between water, oil slicks, and interference, such as algae. In [28] a solution is presented for automated detection while using object detection based on RGB cameras mounted on a UAV.…”

Section: Literaturementioning

confidence: 99%

Oil Spill Detection Using Machine Learning and Infrared Images

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

Section: Literaturementioning

confidence: 99%

Oil Spill Detection Using Machine Learning and Infrared Images

et al. 2020

View full text Add to dashboard Cite

show abstract

“…A series of parameters were used to train the model using the training samples. The 3-fold cross-validation was used to the training samples [35], which helped select the best combination of parameters. Each of the selected methods was tested using the test samples to calculate the accuracy.…”

Section: Overall Proceduresmentioning

confidence: 99%

A Spectral Feature Based Convolutional Neural Network for Classification of Sea Surface Oil Spill

Liu

et al. 2019

IJGI

View full text Add to dashboard Cite

Spectral characteristics play an important role in the classification of oil film, but the presence of too many bands can lead to information redundancy and reduced classification accuracy. In this study, a classification model that combines spectral indices-based band selection (SIs) and one-dimensional convolutional neural networks was proposed to realize automatic oil films classification using hyperspectral remote sensing images. Additionally, for comparison, the minimum Redundancy Maximum Relevance (mRMR) was tested for reducing the number of bands. The support vector machine (SVM), random forest (RF), and Hu’s convolutional neural networks (CNN) were trained and tested. The results show that the accuracy of classifications through the one dimensional convolutional neural network (1D CNN) models surpassed the accuracy of other machine learning algorithms such as SVM and RF. The model of SIs+1D CNN could produce a relatively higher accuracy oil film distribution map within less time than other models.

show abstract

“…An oil spill accident results in the rapid leakage of a large amount of oil. Oil slicks caused by accidents float to the surface of the sea, wreaking havoc on the marine environment (Zhao et al, 2018). Some oil pollution is caused not by ship collisions, but by routine ship operations such as tank cleaning and engine effluent discharges.…”

Section: Introductionmentioning

confidence: 99%

Mapping Oil Spills on Sea Surface From Sentinel 2 Images Using Principal Components and Categorical Boosting

Argamosa

Blanco

Reyes

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. A large oil spill in Iloilo Straight that occurred on July 3, 2020, as well as a possible deliberate, small but frequent oil spill and surfactant contamination in Manila Bay, were mapped. The method employs the Sentinel 2-1C image, which is transformed into principal components to reveal the presence of oil spills and possibly surfactants. Additionally, a gradient boosting algorithm was trained to discriminate between pixels that were contaminated with oil and those that were not. The multi-band image with three principal components with a 99% cumulative explained variance ratio highlights the occurrence of an oil spill in Iloilo Straight. Further, the classified image produced by pixel-based classification clearly distinguishes between water and oil pixels in the said area. The methodology was applied to a Sentinel 2-1C image of Manila Bay, with pixels observed/identified as oil and classified as well. The highest density of supposedly oil-contaminated pixels (large or small but frequent) was observed on the eastern side of Manila Bay (Bataan). While there were no documented oil spills concurrent to the satellite image used, historical reports on the area indicate that the likelihood of an oil spill is extremely high due to the massive amount of shipping activity. Pixels supposedly contaminated by oil spills also occur in areas near ports where oil spills could occur as a result of ship operations. Pixels with the same properties as oil contamination are also visible in areas adjacent to fishponds and aquaculture, where phytoplankton and fish contribute to surfactant contamination.

show abstract

Evaluation of the Ability of Spectral Indices of Hydrocarbons and Seawater for Identifying Oil Slicks Utilizing Hyperspectral Images

Cited by 17 publications

References 27 publications

Oil Spill Detection Using Machine Learning and Infrared Images

Oil Spill Detection Using Machine Learning and Infrared Images

A Spectral Feature Based Convolutional Neural Network for Classification of Sea Surface Oil Spill

Mapping Oil Spills on Sea Surface From Sentinel 2 Images Using Principal Components and Categorical Boosting

Contact Info

Product

Resources

About