Detection of oil slicks in SAR satellite images using Otsu-Bradley’s thresholding method

Majlesi Journal of Electrical Engineering

مقاله 1، دوره 19، شماره 2، شهریور 2025، صفحه 1-9   

نوع مقاله: Reseach Article

شناسه دیجیتال (DOI): 10.57647/j.mjee.2025.1902.24

نویسندگان

Farzane Mahdikhani؛ Mohammadreza Hassannejad Bibalan^*

Department of Electrical Engineering, Imam Khomeini International University, Qazvin, Iran.

چکیده

This paper proposes a novel thresholding method for oil slick detection from synthetic aperture radar (SAR)
images using modified Otsu and Bradley’s approaches. The existence of oil sources in the seas causes
hydrocarbon stains to appear on the surface of the seas and as a result, it leads to a decrease in the quality
of these waters. Oil slicks are distinguished from the sea surface through the utilization of a combined
Otsu-Bradley’s quantization technique, logical operators, and averaging the input image, while categorizing the classes based on the geometrical, textural, and radiometric properties of the images. We aim to enhance the identification of oil spills by utilizing remote sensing techniques, SAR satellite imagery processing, thresholding methods, and extracting geometric and textural features. We performed the classification process several times, and KNN classification method revealed an accuracy of 94.9%. Furthermore, KNN achieved a precision of 92.4%, so we repeated the classification using two selected features, area and entropy to reach a precision of 96.36%.

کلیدواژه‌ها

Oil slick detection؛ SAR satellite images؛ Texture features؛ Geometric features؛ Otsu-Bradley thresholding

مراجع

1. Niyazi, M. Majidi Nezhad, A. Heydari, D. Astiaso Garcia, and G. Sylaios. "A principal component analysis methodology of oil spill detection and monitoring using satellite remote sensing sensors." Remote Sensing15, no. 5 (2023): 1460. 10.3390/rs15051460
2. Qiqi, et al "Oil spill contextual and boundary-supervised detection network based on marine SAR images." IEEE Transactions on Geoscience and Remote Sensing60 (2021): 10.1109/TGRS.2021.3115492
3. Alaa, M. Alkasassbeh, M. Braik, and H. Abu Ayyash. "Detection of oil spills in SAR images using threshold segmentation algorithms." International Journal of Computer Applications57, no. 7 (2012). 10.5120/9125-3291
4. Aghaei, G. Akbarizadeh, and A. Kosarian, “GreyWolfLSM: An accurate oil spill detection method based on level set method from synthetic aperture radar imagery,” Eur. J. Remote Sens., pp. 1‒18, 2022. 10.1080/22797254.2022.2037468
5. W. Mdakane, and W. Kleynhans. "Feature selection and classification of oil spill from vessels using Sentinel-1 wide–swath synthetic aperture radar data." IEEE Geoscience and Remote Sensing Letters19 (2020): 1-5. 10.1109/LGRS.2020.3025641
6. Marghany, “Oil spill pollution automatic detection from MultiSAR satellite data using genetic algorithm,” Adv. Geosci. Remote Sensing, pp. 51–71, 2014. 10.5772/58572
7. Moroni, G. Pieri, and M. Tampucci, “Environmental decision support systems for monitoring small scale oil spills: Existing solutions, best practices and current challenges,” Journal of Marine Science and Engineering, vol. 7, no. 1, p. 19, 2019. 10.3390/jmse7010019
8. Zhu, Q., Zhang, Y., Li, Z., Yan, X., Guan, Q., Zhong, Y., Zhang, L. and Li, D., 2021. “Oil spill contextual and boundary-supervised detection network based on marine SAR images.”, IEEE Transactions on Geoscience and Remote Sensing, 60, pp.1-10. 1109/TGRS.2021.3115492
9. Zeng, K., and Wang, Y., 2020."A deep convolutional neural network for oil spill detection from spaceborne SAR images," Remote Sensing, vol. 12, no. 6, p. 1015. 10.3390/rs12061015
10. Nieto-Hidalgo, M., Gallego, A.J., Gil, P. and Pertusa, A., 2018. "Two-stage convolutional neural network for ship and spill detection using SLAR images," IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 9, pp. 5217-5230. 1109/TGRS.2018.2812619
11. Cantorna, D., Dafonte, C., Iglesias, A. and Arcay, B., 2019. "Oil spill segmentation in SAR images using convolutional neural networks. A comparative analysis with clustering and logistic regression algorithms," Applied Soft Computing, Vol. 84, p. 105716. 11016/j.asoc.2019.105716
12. Shaban, M., Salim, R., Abu Khalifeh, H., Khelifi, A., Shalaby, A., El-Mashad, S., Mahmoud, A., Ghazal, M. and El-Baz, A., 2021. “A deep-learning framework for the detection of oil spills from SAR data.” Sensors, 21(7), p.2351. 10.3390/s21072351
13. Song, D., Zhen, Z., Wang, B., Li, X., Gao, L., Wang, N., Xie, T. and Zhang, T., 2020. "A novel marine oil spillage identification scheme based on convolution neural network feature extraction from fully polarimetric SAR imagery," IEEE Access, Vol. 8, pp. 59801-59820. 1109/ACCESS.2020.2979219
14. Mera, V. Bolon-Canedo, J. M. Cotos, and A. Alonso-Betanzos, “On the use of feature selection to improve the detection of sea oil spills in SAR images,” Comput. Geosci., vol. 100, pp. 166–178, Mar. 2017. 10.1016/j.cageo.2016.12.013
15. [C. Huang, X. Li, and Y. Wen, “An Otsu image segmentation based on fruitfly optimization algorithm,” Alexandria Eng. J., vol. 60, no. 1, pp.183–188, 2020. 1016/j.aej.2020.06.054
16. Abdusalomov, M. Mukhiddinov, O. Djuraev, U. Khamdamov, T.K. Whangbo, “Automatic salient object extraction based on locally adaptive thresholding to ​ generate tactile graphics, ” Applied Sciences, 10 (2020) 3350. 10.3390/app10103350
17. Jafarzadeh, M. Mahdianpari, S. Homayouni, F. Mohammadi-manesh, and M. Dabboor, “Oil spill detection from synthetic aperture radar earth observations: A meta-analysis and comprehensive review,” Giscience Remote Sens., vol. 58, no. 7, pp. 1022–1051, Oct. 2021. 10.1080/15481603.2021.1952542
18. Sara, M. Akter, and M. S. Uddin, ‘‘Image quality assessment through FSIM, SSIM, MSE and PSNR—A comparative study,’’ J. Comput. Commun., vol. 7, no. 3, pp. 8–18, 2019. 10.4236/jcc.2019.73002
19. K. Mall, P. K. Singh, and D. Yadav, ‘‘GLCM based feature extraction and medical X-ray image classification using machine learning techniques,’’ in Proc. IEEE Conf. Inf. Commun. Technol., Dec. 2019, pp. 1–6. 10.1109/CICT48419.2019.9066263
20. C. Sen, M. Hajra, and M. Ghosh, ‘‘Supervised classification algorithms in machine learning: A survey and review,’’ in Advances in Intelligent Systems and Computing. Singapore: Springer Jul. 2019, pp. 99–111. 10.1007/978-981-13
21. Jafarzadeh, M. Mahdianpari, S. Homayouni, F. Mohammadimanesh, and M. Dabboor. "Oil spill detection from Synthetic Aperture Radar Earth observations: A meta-analysis and comprehensive review." GIScience & Remote Sensing58, no. 7 (2021): 1022-1051. 10.1080/15481603.2021.1952542
22. Kurkova, M. Sanguineti, “Approximation of classifiers by deep perceptron networks,” Neural Net. 165, 654–661 (2023). 10.1016/j.neunet.2023.06.004
23. MurtiRawat, S. Panchal, V. K. Singh, and Y. Panchal, ‘‘Breast cancer detection using K-nearest neighbors, logistic regression and ensemble learning,’’ in Proc. Int. Conf. Electron. Sustain. Commun. Syst., Jul. 2020, pp. 534–540. 10.1109/ICESC48915.2020.9155783