Indian Journal of Geo-Marine Sciences

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Application of CNN Based Image Classification Technique for Oil Spill Detection


Affiliations
1 Department of Civil Engineering, National Institute of Technology Silchar, Assam – 788 010, India
2 Department of Civil Engineering, National Institute of Technology Goa, Farmagudi, Goa – 403 401, India
 

Marine water pollution due to oil spills is a common threat to the environment worldwide because of its harmful impact on the economy and environment. Remote Sensing (RS) and Geographic Information Systems (GIS) are well-known tools for collecting satellite data which helps in remote oil spill identification. Synthetic Aperture Radar (SAR) images through various satellite missions are the mainly used data to identify oil spills. Many Artificial Neural Networks (ANN) and Machine Learning (ML) models integrated with RS and GIS have been originated and applied to identify and monitor oil spills. Deep Learning (DL) methods have recently become popular for their outstanding performance in research for image classification challenges, and the same is being used in the present study. An oil spill detection model using the Convolutional Neural Network (CNN) algorithm is presented in this work. CNN can extract features from a large dataset, and these features can be used to categorize images into different classes. The proposed model was compared with other existing models. The accuracy, precision, and recall achieved by this study are 99.06 %, 98.15 %, and 100 %, respectively. The proposed model outperformed the other existing work with an accuracy of 99.06 % and a precision of 98.15 %.

Keywords

Convolutional Neural Network, Geographic Information System, Image classification, Oil spill, Synthetic Aperture Radar
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  • Smith L C J, Smith M L & Ashcroft P A, Analysis of Environmental and Economic Damages from British Petroleum's Deepwater Horizon Oil Spill, Albany Law Review, 74 (1) (2011) 563-585. https://dx.doi.org/10.2139/ssrn.1653078

  • Brekke C & Solberg A H S, Oil spill detection by satellite remote sensing, Remote Sens Environ, 95 (1) (2005) 1-13. https://doi.org/10.1016/j.rse.2004.11.015

  • Solberg A H S, Brekke C & Husoy P O, Oil Spill Detection in Radarsat and Envisat SAR images, IEEE Trans Geosci Remote Sens, 45 (3) (2007) 746-754. https://doi.org/10.1109/TGRS.2006.887019

  • Topouzelis K N, Oil Spill Detection by SAR Images: Dark Formation Detection, Feature Extraction and Classification Algorithms, Sensors, 8 (10) (2008) 6642–6659. https://doi.org/10.3390/s8106642

  • Solberg A H S, Remote Sensing of Ocean Oil-Spill Pollution, Proc IEEE, 100 (10) (2012) 2931-2945. https://doi.org/10.1109/JPROC.2012.2196250

  • Uhlmann S & Kiranyaz S, Classification of dualand single polarized SAR images by incorporating visual features, ISPRS J Photogramm Remote Sens, 90 (2014) 10-22. http://dx.doi.org/10.1016/j.isprsjprs.2014.01.005

  • Fingas M & Brown C, Review of oil spill remote sensing, Mar Pollut Bull, 83 (1) (2014) 9–23. https://doi.org/10.1016/j.marpolbul.2014.03.059

  • Kolokoussis P & Karathanassi V, Oil Spill Detection and Mapping Using Sentinel 2 Imagery, J Mar Sci Eng, 6 (1) (2018). https://doi.org/10.3390/jmse6010004

  • Rajendran S, Vethamony P, Sadooni F N, Al-Kuwari H A S, Al-K J A, et al., Detection of Wakashio oil spill off Mauritius using Sentinel-1 and 2 data: Capability of sensors, image transformation methods and mapping, Environ Pollut, 274 (2021) 116618. https://doi.org/10.1016/j.envpol.2021.116618

  • Mahmoud A S, Mohamed S A & El-K R A, Oil Spill Identification based on Dual Attention UNet Model Using Synthetic Aperture Radar Images, J Indian Soc Remote Sens, 51 (2023) 121–133. https://doi.org/10.1007/s12524-022-01624-6

  • Vijayakumar S, Computational Techniques of Oil Spill Detection in Synthetic Aperture Radar Data: Review Cases, Recent Oil Spill Challenges That Require More Attention, 5 (2023) https://doi.org/10.5772/intechopen.108115

  • Hidalgo M N, 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, 56 (9) (2018) 5217-5230. https://doi.org/10.1109/TGRS.2018.2812619

  • Fingas M & Brown C, Review of oil spill remote sensing, Spill Sci Technol Bull, 4 (4) (1997) 199-208. https://doi.org/10.1016/S1353-2561(98)00023-1

  • Espedal H A & Johannessen O M, Cover: Detection of oil spills near offshore installations using synthetic aperture radar (SAR), Int J Remote Sens, 21 (11) (2000) 2141-2144. https://doi.org/10.1080/01431160050029468

  • Kapustin I A, Shomina O V, Ermoshkin A V, Bogatov N A, Kupaev A V, et al., On Capabilities of Tracking Marine Surface Currents Using Artificial Film Slicks, Remote Sens, 11 (7) (2019) 840. https://doi.org/10.3390/rs11070840

  • Solberg A H S, Storvik G, Solberg R & Volden E, Automatic Detection of Oil Spills in ERS SAR Images, IEEE Trans Geosci Remote Sens, 37 (4) (1999) 1916-1924. https://doi.org/10.1109/36.774704

  • Frate F D, Petrocchi A, Lichtenegger J & Calabresi G, Neural Networks for Oil Spill Detection Using ERS-SAR Data, IEEE Trans Geosci Remote Sens, 38 (5) (2000) 2282-2287. https://doi.org/10.1109/36.868885

  • Fiscella B, Giancaspro A, Nirchio F, Pavese P & Trivero P, Oil Spill Detection using marine SAR images, Int J Remote Sens, 21 (18) (2000) 3561-3566. https://doi.org/10.1080/014311600750037589

  • Kanaa T F N, Tonye E, Mercier G, Onana V P & Rudant J P, Multiscale Segmentation of Oil Slick in SAR Images Based on Morphological Pyramid, Proc 2004 Envisat & ERS Symposium (Salzburg, Austria), 2004, pp. 6-10.

  • Nirchio F, Sorgente M, Giancaspro A, Biamino W, Parisato E, et al., Automatic detection of oil spills from SAR images, Int J Remote Sens, 26 (6) (2005) 1157-1174. https://doi.org/10.1080/01431160512331326558

  • Karathanassi V, Topouzelis K, Pavlakis P & Rokos D, An object-oriented methodology to detect oil spills, Int J Remote Sens, 27 (23) (2006) 5235-5251. https://doi.org/10.1080/01431160600693575

  • 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, 62 (4) (2007) 264-270. https://doi.org/10.1016/j.isprsjprs.2007.05.003

  • Stathakis D, Topouzelis K & Karathanassi V, SPIE Remote Sensing (Stockholm, Sweden), 2006, pp. 342-350. https://doi.org/10.1117/12.688149

  • Brekke C & Solberg A, Feature Extraction for Oil Spill Detection Based on SAR Images, In: Image Analysis. SCIA 2005. Lecture Notes in Computer Science, Vol 3540, edited by Kalviainen H, Parkkinen J & Kaarna A, (Springer, Berlin, Heidelberg,) 2005, pp. 75-84. https://doi.org/10.1007/11499145_9

  • Huang B, Li H & Huang X, A level set method for oil slick segmentation in SAR images, Int J Remote Sens, 26 (6) (2005) 1145-1156. https://doi.org/10.1080/014311605123 31326747

  • Topouzelis K & Psyllos A, Oil spill feature selection and classification using decision tree forest on SAR image data, ISPRS J Photogramm Remote Sens, 68 (2012) 135-143. https://doi.org/10.1016/j.isprsjprs.2012.01.005

  • Singha S, Bellerby T & Trieschmann O, Satellite Oil Spill Detection Using Artificial Neural Networks, IEEE J Sel Top Appl Earth Obs Remote Sens, 6 (6) (2013) 2355-2363. https://doi.org/10.1109/JSTARS.2013.2251864

  • Guo H, Wu D & An J, Discrimination of Oil Slicks and Look-alikes in Polarimetric SAR Images Using CNN, Sensors, 17 (8) (2017) 1837. https://doi.org/10.3390/s17081837

  • Gallego A J, Gil P, Pertusa A & Fisher R B, Segmentation of Oil Spills on Side-Looking Airborne Radar Imagery with Autoencoders, Sensors, 18 (3) (2018) 797. https://doi.org/10.3390/s18030797

  • Krestenitis M, Orfanidis G, Ioannidis K, Avgerinakis K, Vrochidis S, et al., Oil Spill Identification from Satellite Images Using Deep Neural Networks, Remote Sens, 11 (15) (2019) 1-22. https://doi.org/10.3390/rs11151762

  • Krestinitis M, Orfanidis G, Ioannidis K, Avgerinakis K, Vrochidis S, et al., Early Identification of Oil Spills in Satellite Images Using Deep CNNs, Paper presented at the Proc 25th International Conference on Multi Media Modeling, MMM (Thessaloniki, Greece), 2019, pp. 424-435. https://doi.org/10.1007/978-3-030-05710-7_35

  • Cantorna D, Dafonte C, Iglesias A & Arcay B, Oil spill segmentation in SAR images using convolutional neural networks. A comparative analysis with clustering and logistic regression algorithms, Appl Soft Comput, 84 (2019) 105716. https://doi.org/10.1016/j.asoc.2019.105716

  • Zeng K & Wang Y, A Deep Convolutional Neural Network for Oil Spill Detection from Spaceborne SAR Images, Remote Sens, 12 (6) (2020) 1015. https://doi.org/10.3390/rs12061015

  • Patterson J & Gibson A, Deep Learning A Practitioner's Approach, 1st Edn, (O'Reilly Media Inc, Sebastopol), 2017, pp. 530.

  • Alzubaidi L, Zhang J, Humaidi A J, Dujaili A A, Duan Y, et al., Review of deep learning: concepts, CNN architectures, challenges, applications, future directions, J Big Data, 8 (53) (2021). https://doi.org/10.1186/s40537-021-00444-8

  • Goodfellow I, Bengio Y & Courville A, Deep Learning, Illustrated Edn, (The MIT Press London, England), 2016, pp. 800.

  • Srivastava N, Hinton G & Krizhevsky A, Dropout: A simple way to prevent neural networks from overfitting, J Mach Learn Res, 15 (2014) 1929-1958.

  • Song D, Zhen Z, Wang B, Li X, Gao L, et al., A Novel Marine Oil Spillage Identification Scheme Based on Convolution Neural Network Feature Extraction From Fully Polarimetric SAR Imagery, IEEE Access, 8 (2020) 59801-59820. https://doi.org/10.1109/ACCESS.2020.2979219


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  • Application of CNN Based Image Classification Technique for Oil Spill Detection

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Authors

K. Das
Department of Civil Engineering, National Institute of Technology Silchar, Assam – 788 010, India
P. Janardhan
Department of Civil Engineering, National Institute of Technology Goa, Farmagudi, Goa – 403 401, India
H. Narayana
Department of Civil Engineering, National Institute of Technology Goa, Farmagudi, Goa – 403 401, India

Abstract


Marine water pollution due to oil spills is a common threat to the environment worldwide because of its harmful impact on the economy and environment. Remote Sensing (RS) and Geographic Information Systems (GIS) are well-known tools for collecting satellite data which helps in remote oil spill identification. Synthetic Aperture Radar (SAR) images through various satellite missions are the mainly used data to identify oil spills. Many Artificial Neural Networks (ANN) and Machine Learning (ML) models integrated with RS and GIS have been originated and applied to identify and monitor oil spills. Deep Learning (DL) methods have recently become popular for their outstanding performance in research for image classification challenges, and the same is being used in the present study. An oil spill detection model using the Convolutional Neural Network (CNN) algorithm is presented in this work. CNN can extract features from a large dataset, and these features can be used to categorize images into different classes. The proposed model was compared with other existing models. The accuracy, precision, and recall achieved by this study are 99.06 %, 98.15 %, and 100 %, respectively. The proposed model outperformed the other existing work with an accuracy of 99.06 % and a precision of 98.15 %.

Keywords


Convolutional Neural Network, Geographic Information System, Image classification, Oil spill, Synthetic Aperture Radar

References