Water Surface Oil Film Inversion Method Based on Infrared Imager
Authors: 
Shuai Xu, Jin Xu, Bo Li, Rong ChenAuthors Info & Claims
Shuai Xu, Jin Xu, Bo Li, Rong ChenAuthors Info & ClaimsPages 97 - 102
Abstract
Oil spills bring great negative impact on marine ecology and social economy. At present, remote sensing technology is an important tool for marine oil spill monitoring. In this paper, a marine oil film inversion method using infrared thermal imager was proposed to support sustainable tracking in the oil spill accident. Firstly, different amounts of oils were added into the experimental containers with the same water. Secondly, visible light images, infrared images and environmental data were collected continuously. Then, the infrared images were segmented for oil film detection by using Otsu, InterModes and ISODATA threshold methods. Finally, the effects of different adaptive threshold methods for oil film detection were compared. The results shown that the oil film inversion effect based on ISODATA threshold performed more superior.
References
[1]
Chen qinsi and Hu song. 2020. Marine Oil Spill Accidents in China's Coastal Zone. Ocean Development and Management, 37, 12, 49-53. https://oversea.cnki.net/kns/detail/detail.aspx?FileName=HKGL202012009&DbName=CJFQ2020.
[2]
Wang Lifeng, Xin Liping, Yu Bo, Ju Lian, Wei Lai and Hu Zhanbo. 2020. A calculation method for oil film area at sea surface based on thermal infrared image, Marine Science Bulletin, 39, 6, 750-760. https://oversea.cnki.net/kns/detail/detail.aspx? FileName=HUTB202006013&DbName=CJFQ2020.
[3]
Chen Yantong, Li Yuyang, Lu Shili and Wang Junsheng.2019.Department of Information Science and Technology. Optics and Precision Engineering, 28, 5, 1165-1176. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=GXJM202005020&DbName= CJFQ2020.
[4]
Wang Lifeng. 2020. Research of the Detection of Oil Film on Sea Surface Based on Thermal Infrared Image. Qingdao University of Technology. https://kns.cnki.net/kcms/detail/detail.aspx?FileName=1021743979.nh&DbName=CMFDTEMP.
[5]
Deng Qianqian. 2019. Oil Spill Area Detection in Infrared Image. Huazhong University of Science and Technology. https://oversea.cnki.net/kns/detail/detail.aspx?FileName=1019616504.nh&DbName=CMFD2020.
[6]
Nobuyuki Otsu. 2007. Threshold Selection Method from Gray-Level Histograms. IEEE Transactions on Systems Man & Cybernetics, 9, 1, 62-66. https://doi.org/10.1109/TSMC.1979.4310076.
[7]
Judith M. S. Prewitt and Mortimer L. Mendelsohn. 1966. The analysis of cell images*. Annals of the New York Academy of Sciences, 128, 3, 1035-1053. https://doi.org/10.1111/j.1749-6632.1965.tb11715.x
[8]
T. W. RIDLER AND S. CALVARD. 1978. Picture Thresholding Using an Iterative Selection Method. IEEE Transactions on Systems, Man, and Cybernetics. 8, 8, 630-632. https://doi.org/10.1109/TSMC.1978.4310039.
Index Terms
- Water Surface Oil Film Inversion Method Based on Infrared Imager
Recommendations
Automatic Detection of Oil Level of Transformer Oil Conservator Based on Infrared Image Segmentation Technology
ICICTA '11: Proceedings of the 2011 Fourth International Conference on Intelligent Computation Technology and Automation - Volume 01Oil level is one of the important monitoring parameters of Power Transformer, it is important to detect the oil level when it is too low or too high in time and take the appropriate measures to ensure the safe of power transformers. To be effective and ...
A graphical exploration of the Deepwater Horizon oil spill
This paper investigates some of the immediate impacts of the Deepwater Horizon oil spill of 2010 on the environment using graphical means. The exploration focuses on the effects of the oil discharge on wildlife, the chemical pollution in the area ...
Oil spill segmentation in SAR images using convolutional neural networks. A comparative analysis with clustering and logistic regression algorithms
AbstractSynthetic aperture radar (SAR) images are a valuable source of information for the detection of marine oil spills. For their effective analysis, it is important to have segmentation algorithms that can delimit possible oil spill areas. ...
Graphical abstractDisplay Omitted
Highlights- The images studied show oil spills of diverse shapes and sizes.
- To evaluate the ...
Comments
Information & Contributors
Information
Published In
November 2022
202 pages
ISBN:9781450397155
DOI:10.1145/3577117
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 25 February 2023
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Funding Sources
- Research start-up funding project of Guangdong Ocean University
- Natural Science Foundation of Guangdong Province
Conference
ICAIP 2022
ICAIP 2022: 2022 6th International Conference on Advances in Image Processing
November 18 - 20, 2022
Zhanjiang, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 17Total Downloads
- Downloads (Last 12 months)12
- Downloads (Last 6 weeks)0
Reflects downloads up to 11 Aug 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format