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http://dx.doi.org/10.26748/KSOE.2019.104

Algorithm to Estimate Oil Spill Area Using Digital Properties of Image  

Jang, Hye-Jin (Department of Naval Architecture and Ocean Systems Engineering, Korea Maritime & Ocean University)
Nam, Jong-Ho (Division of Naval Architecture and Ocean Systems Engineering, Korea Maritime & Ocean University)
Publication Information
Journal of Ocean Engineering and Technology / v.34, no.1, 2020 , pp. 46-54 More about this Journal
Abstract
Oil spill accidents at sea result in a wide range of damages, including the destruction of ocean environments and ecosystems, as well as human illnesses by the generation of harmful gases caused by phase changes in crude oil. When an oil spill occurs, an immediate initial action should be performed to minimize the potential damage. Existing studies have attempted to identify crude oil spillage by calculating the crude oil spill range using synthetic aperture radar (SAR) satellite images. However, SAR cannot capture rapidly evolving events because of its low acquisition frequency. Herein, an algorithm for estimating an oil spill area from an image obtained using a digital camera is proposed. Noise that may occur in the image when it is captured is first eliminated by preprocessing, and then the image is analyzed. After analyzing the characteristics of the digital image, a strategy to binarize an image using the color, saturation, or lightness contained in it is adopted. It is found that the oil spill area can be readily estimated from a digital image, allowing for a faster analysis than any conventional method. The usefulness of the oil spill area measurement was confirmed by applying the developed algorithm to various oil spill images.
Keywords
Oil spill; Digital image; Image processing; Binarization; Image histogram;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 Bradley, D., & Roth, G. (2007). Adaptive Thresholding Using the Integral Image. Journal of Graphic Tools, 12(2), 13-21. https://doi.org/10.1080/2151237X.2007.10129236   DOI
2 Fiscella, B., Giancaspro, A., Nirchi, F., Pavese, P., & Trivero, P. (2000). Oil Spill Detection Using Marine SAR Images. International Journal of Remote Sensing, 21(18), 3561-3566. https://doi.org/10.1080/014311600750037589   DOI
3 International Petroleum Industry Environmental Conservation Association (IPIECA). (2016). Aerial Observation of Oil Spills at Sea. Retrieved 25 August 2019 from http://www.ipieca.org/resources/good-practice/aerial-observation-of-oil-spills-at-sea
4 International Tanker Owners Pollution Federation Limited (ITOPF). (2014). Documents & Guides. Retrieved 12 March 2019 from https://www.itopf.org/kr/knowledge-resources/documentsguides
5 Kim, T., Park, K., Lee, M., Park, J., Hong, S., Kim, K., & Chang, E. (2013). Application of Bimodal Histogram Method to Oil Spill Detection from a Satellite Synthetic Aperture Radar Image. Korean Journal of Remote Sensing, 29(6), 645-655. https://doi.org/10.7780/kjrs.2013.29.6.7   DOI
6 National Oceanic and Atmospheric Administration (NOAA). (2016). How Do We Use Satellite Data During Oil Spills? Retrieved 27 February 2019 from https://response.restoration.noaa.gov/about/media/how-do-we-use-satellite-data-during-oil-spills.html
7 Open Source Computer Vision (OpenCV). (2019). Online Documentation. Retrieved 18 September 2019 from https://docs.opencv.org
8 Gonzalez, R., & Woods, R. (2017). Digital Image Processing. 4th Edition, Pearson.
9 Petrou, M., & Petrou, C. (2010). Image Processing: the Fundamentals. John Wiley and Sons.
10 Schvartzman, I., Havivi, S., Maman, S., Rotman, S., & Blumberg, D. (2016). Large Oil Spill Classification Using SAR Images Based on Spatial Histogram. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B8, 1183-1186. https://doi.org/10.5194/isprsarchives-XLI-B8-1183-2016
11 Topouzelis, K.N. (2008). Oil Spill Detection by SAR Images: Dark Formation Detection, Feature Extraction and Classification Algorithms. Sensors, 8(10), 6642-6659. https://doi.org/10.3390/s8106642   DOI
12 Xu, L., Shafiee, M., Wong, A., Li, F., Wang, L., & Clausi, D. (2015). Oil Spill Candidate Detection from SAR Imagery Using a Thresholding-Guided Stochastic Fully Connected Conditional Random Field Model. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, 79-86.
13 Otsu, N. (1979). A Threshold Selection Method from Gray-level Histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62-66. https://doi.org/10.1109/tsmc.1979.4310076   DOI