10.57647/j.mjee.2025.1902.24

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

PDF
  • Farzane Mahdikhani1,
  • Mohammadreza Hassannejad Bibalan*1,
  1. Department of Electrical Engineering, Imam Khomeini International University, Qazvin, Iran

Received: 2025-03-12

Revised: 2025-04-07

Accepted: 2025-04-30

Published in Issue 2025-06-01

Copyright (c) 2025 Farzane Mahdikhani, Mohammadreza Hassannejad Bibalan (Author)

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Mahdikhani, F., & Hassannejad Bibalan, M. (2025). Detection of oil slicks in SAR satellite images using Otsu-Bradley’s thresholding method. Majlesi Journal of Electrical Engineering, 19(2 (June 2025). https://doi.org/10.57647/j.mjee.2025.1902.24
Crossref
Scopus
Google Scholar
Europe PMC

PDF views: 134

Abstract

 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%. 

Keywords

  • Oil slick detection,
  • SAR satellite images,
  • Texture features,
  • Geometric features,
  • Otsu-Bradley thresholding
PDF

References

  1. A. 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 Sensing 15, no. 5 (2023): 1460. 10.3390/rs15051460
  2. Z. Qiqi, et al "Oil spill contextual and boundary-supervised detection network based on marine SAR images." IEEE Transactions on Geoscience and Remote Sensing 60 (2021): 10.1109/TGRS.2021.3115492
  3. S. Alaa, M. Alkasassbeh, M. Braik, and H. Abu Ayyash. "Detection of oil spills in SAR images using threshold segmentation algorithms." International Journal of Computer Applications 57, no. 7 (2012). 10.5120/9125-3291
  4. N. 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. L. 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 Letters 19 (2020): 1-5. 10.1109/LGRS.2020.3025641
  6. M. 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. D. 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. 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. 10.1109/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. 10.1016/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. 10.1109/ACCESS.2020.2979219
  14. D. 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. 10.1016/j.aej.2020.06.054
  16. A. 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. H. 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. U. 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. P. 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. P. 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. H. 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 Sensing 58, no. 7 (2021): 1022-1051. 10.1080/15481603.2021.1952542
  22. V. Kurkova, M. Sanguineti, “Approximation of classifiers by deep perceptron networks,” Neural Net. 165, 654–661 (2023). 10.1016/j.neunet.2023.06.004
  23. R. 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