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JGST 2023, 12(2): 30-46 Back to browse issues page
Oil spill detection using in Sentinel-1 satellite images based on Deep learning concepts
Saeid Dehghani, Mehdi Akhoondzadeh Hanzaei *
Abstract:   (409 Views)
Awareness of the marine area is very important for crisis management in the event of an accident. Oil spills are one of the main threats to the marine and coastal environments and seriously affect the marine ecosystem and cause political and environmental concerns because it seriously affects the fragile marine and coastal ecosystem. The rate of discharge of pollutants and its related effects on the marine environment are important parameters in assessing the quality of seawater. Effective monitoring, early detection and estimation of the size of these spots are the first and most important step for a successful cleanup operation and that is essential for the relevant authorities to react in a timely manner and limit marine pollution and prevent further damage. Synthetic-aperture radar (SAR) sensors are a very good choice for this purpose due to their effective operation capability regardless of weather conditions and ambient lighting conditions and large area land cover. Black spots related to oil spills can be clearly detected by SAR sensors, but their visual distinction is a challenging goal. The study used artificial aperture radar (SAR) images from the Sentinel-1 satellite to detect oil spills that distributed by European Space Agency (ESA) via the Copernicus Open Access Hub. This paper provides a deep learning framework for identifying oil spills based on a very large data set from around the world, and using the structure of U-Net, DeepLabV3 + and Fc-DenseNet convolutional networks, it classifies images into two classes. In this study, by changing the loss function and deleting single-class images, much better results were obtained than previous similar works. The IoU results for the U-Net, DeepLabV3 +, and FC-DenseNet models were 0.547, 0.513, and 0.545, respectively.
Article number: 3
Keywords: Oil spill, Covolutional Neural Network, Sentinel-1 Satellite, U-Net, DeepLabV3+, Fc-DenseNet
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Type of Study: Research | Subject: Photo&RS
1. Calabresi, G.; Del Frate, F.; Lichtenegger, J.;Petrocchi, A.; Trivero, P. Neural networks for oil spill detection using ERS-SAR data. In Proceedings of the IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293), Hamburg, Germany, 28 June-2 July 1999; Volume 38, pp. 2282-2287. [DOI:10.1109/36.868885]
2. Brekke, C.; Solberg, A.H. Oil spill detection by satellite remote sensing. Remote Sens. Environ. 2005, 95, 1-13. [DOI:10.1016/j.rse.2004.11.015]
3. Solberg, A.H.; Brekke, C.; Husoy, P.O. Oil spill detection in Radarsat and Envisat SAR images. IEEE Trans.Geosci. Remote Sens. 2007, 45, 746-755. [DOI:10.1109/TGRS.2006.887019]
4. Topouzelis, K. Oil spill detection by SAR images:Dark formation detection, feature extraction and classification algorithms. Sensors 2008, 8, 6642-6659. [DOI:10.3390/s8106642]
5. Solberg, A.H.S. Remote sensing of ocean oil-spill pollution. Proc. IEEE 2012, 100, 2931-2945. [DOI:10.1109/JPROC.2012.2196250]
6. Fingas, M.; Brown, C. Review of oil spill remote sensing. Mar. Pollut. Bull. 2014, 83, 9-23. [DOI:10.1016/j.marpolbul.2014.03.059]
7. Fingas, M.F.; Brown, C.E. Review of oil spill remote sensing. Spill Sci. Technol. Bull. 1997, 4, 199-208. [DOI:10.1016/S1353-2561(98)00023-1]
8. Kapustin, I.A.; Shomina, O.V.; Ermoshkin, A.V.;Bogatov, N.A.; Kupaev, A.V.; Molkov, A.A.; Ermakov, S.A.On Capabilities of Tracking Marine Surface Currents Using Artificial Film Slicks. Remote Sens. 2019, 11, 840. [DOI:10.3390/rs11070840]
9. Espedal, H.A.; Johannessen, J.A. Detection of oil spills near offshore installations using synthetic aperture radar (SAR). Int. J. Remote Sens. 2000, 11, 2141-2144. [DOI:10.1080/01431160050029468]
10. Solberg, A.S.; Storvik, G.; Solberg, R.; Volden,E. Automatic detection of oil spills in ERS SAR images.IEEE Trans. Geosci. Remote Sens. 1999, 37, 1916-1924. [DOI:10.1109/36.774704]
11. Fiscella, B.; Giancaspro, A.; Nirchio, F.;Pavese, P.; Trivero, P. Oil spill detection using marine SAR images.Int. J. Remote Sens. 2000, 21, 3561-3566. [DOI:10.1080/014311600750037589]
12. Espedal, H. Satellite SAR oil spill detection using wind history information. Int. J. Remote Sens. 1999, [DOI:10.1080/014311699213596]
13. 20, 49-65.
14. Fiscella, B.; Giancaspro, A.; Nirchio, F.;Trivero P. Oil spill detection using marine SAR images.Int. J. Remote Sens. 2000, 21, 3561-3566. [DOI:10.1080/014311600750037589]
15. De Souza, D.; Neto, A.; Da Mata, W. Intelligent System for Feature Extraction of Oil Slicks in SAR Images: Speckle Filter Analysis. Lecture Notes in Computer Science Vol. 4233, 13th International Conference, ICONIP 2006, Hong Kong, China, October 3-6, 2006.
16. Keramitsoglou, I.; Cartalis, C.;Kiranoudis, C.T. Automatic identification of oil spills on satellite images.
17. Environ. Model. Softw. 2006, 21, 640-652. [DOI:10.1016/j.envsoft.2004.11.010]
18. Karathanassi, V.; Topouzelis, K.;Pavlakis, P.; Rokos, D. An object-oriented methodology to detect oil spills.Int. J. Remote Sens. 2006, 27, 5235-5251. [DOI:10.1080/01431160600693575]
19. Konik, M.; Bradtke, K. Object-oriented approach to oil spill detection using ENVISAT ASAR images. ISPRS J.Photogramm. Remote Sens. 2016,118, 37-52 [DOI:10.1016/j.isprsjprs.2016.04.006]
20. Topouzelis, K.; Psyllos, A. Oil spill feature selection and classification using decision tree forest on SAR
21. image data. ISPRS J. Photogramm. Remote Sens. 2012, 68, 135-143. [DOI:10.1016/j.isprsjprs.2012.01.005]
22. Mercier, G.; Girard-Ardhuin, F. Partially supervised oil-slick detection by SAR imagery using kernel expansion. IEEE Trans. Geosci. Remote Sens. 2006, 44, 2839-2846. [DOI:10.1109/TGRS.2006.881078]
23. Topouzelis, K.; Karathanassi, V.; Pavlakis, P.; Rokos, D. Detection and discrimination between oil spills and look-alike phenomena through neural networks. ISPRS J. Photogramm. Remote Sens.2007, 62, 264-270. [DOI:10.1016/j.isprsjprs.2007.05.003]
24. Del Frate, F.; Petrocchi, A.;Lichtenegger, J.; Calabresi, G. Neural networks for oil spill detection using ERS-SAR data. IEEE Trans. Geosci. Remote Sens. 2000, 38, 2282-2287. [DOI:10.1109/36.868885]
25. Song, D.; Ding, Y.; Li, X.; Zhang, B.; Xu, M. Ocean oil spill classification with RADARSAT-2 SAR based on an optimized wavelet neural network. Remote Sens. 2017, 9, 799. [DOI:10.3390/rs9080799]
26. Stathakis, D.; Topouzelis, K.; Karathanassi, V. Large-scale feature selection using evolved neural networks.In Image and Signal Processing for Remote Sensing XII, Proceedings of the International Society for Optics and Photonics, Stockholm, Sweden, 2006; SPIE: Bellingham, WA USA, 2006; Volume 6365, p. 636513. [DOI:10.1117/12.688149]
27. Orfanidis, G.; Ioannidis, K.; Avgerinakis, K.; Vrochidis, S.; Kompatsiaris, I. A deep neural network for oil spill semantic segmentation in SAR images. In Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece, 7-10 October 2018; IEEE: Piscataway, NJ, USA, 2018; pp. 3773-3777. [DOI:10.1109/ICIP.2018.8451113]
28. Krestenitis, M.; Orfanidis, G.; Ioannidis, K.; Avgerinakis, K.; Vrochidis, S.; Kompatsiaris, I. Early Identification of Oil Spills in Satellite Images Using Deep CNNs. In Proceedings of the International Conference on Multimedia Modeling, Thessaloniki, Greece, 8-11 January 2019; Springer: Berlin/Heidelberg, Germany,2019; pp. 424-435. [DOI:10.1007/978-3-030-05710-7_35]
29. Krestenitis, M., Orfanidis, G., Ioannidis, K., Avgerinakis, K., Vrochidis, S., & Kompatsiaris, I. (2019). Oil Spill Identification from Satellite Images Using Deep Neural Networks. Remote Sensing, 11(15), 1762. [DOI:10.3390/rs11151762]
30. Copernius Open Access Hub. Available online: https://scihub.copernicus.eu/ (accessed on 20 July 2020).
31. Ronneberger, O.; Fischer, P.; Brox, T. U-net: Convolutional networks for biomedical image segmentation.In International Conference on Medical Image Computing and Computer-Assisted Intervention; Springer:Berlin/Heidelberg, Germany, 2015; pp. 234-241. [DOI:10.1007/978-3-319-24574-4_28]
32. Iglovikov, V.; Shvets, A. Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv 2018, arXiv:1801.05746.
33. Iglovikov, V.; Mushinskiy, S.; Osin, V. Satellite imagery feature detection using deep convolutional neural network: A kaggle competition. arXiv 2017, arXiv:1706.06169.
34. Long, J.; Shelhamer, E.; Darrell, T. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 7-12 June 2015;pp. 3431-3440. [DOI:10.1109/CVPR.2015.7298965]
35. Shaban, M.; Salim, R.; Abu Khalifeh, H.; Khelifi, A.; Shalaby, A.; El-Mashad, S.; Mahmoud, A.; Ghazal, M.; El-Baz, A. A Deep-Learning Framework for the Detection of Oil Spills from SAR Data. Sensors 2021, 21, 2351. [DOI:10.3390/s21072351]
36. Calabresi, G.; Del Frate, F.;Lichtenegger, J.; Petrocchi, A.; Trivero, P. Neural networks for oil spill detection using ERS-SAR data.In Proceedings of the IEEE 1999 International Geoscience and Remote Sensing Symposium. IGARSS'99 (Cat. No.99CH36293),Hamburg, Germany, 28 June-2 July 1999; Volume 38, pp. 2282-2287. [DOI:10.1109/36.868885]
37. Baek,W.; Jung, H.; Kim, D. Oil spill detection of Kerch strait in November 2007 from dual-polarized TerraSAR-X image using artificial and convolutional neural network regression models. J. Coast. Res. 2020, 102, 137-144. [DOI:10.2112/SI102-017.1]
38. Nieto-Hidalgo, M.; Gallego, A.-J.; Gil, P.; Pertusa, A. Two-Stage Convolutional Neural Network for Ship and Spill Detection Using SLAR Images. IEEE Trans. Geosci. Remote Sens. 2018, 56, 5217-5230. [DOI:10.1109/TGRS.2018.2812619]
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Dehghani S, Akhoondzadeh Hanzaei M. Oil spill detection using in Sentinel-1 satellite images based on Deep learning concepts. JGST 2023; 12 (2) :30-46
URL: http://jgst.issge.ir/article-1-1045-en.html

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Volume 12, Issue 2 (1-2023) Back to browse issues page
نشریه علمی علوم و فنون نقشه برداری Journal of Geomatics Science and Technology