Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review
Remote sensing technologies and machine learning (ML) algorithms play an increasingly important role in accurate detection and monitoring of oil spill slicks, assisting scientists in forecasting their trajectories, developing clean-up plans, taking timely and urgent actions, and applying effective t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/20/3338 |
| _version_ | 1797551140216242176 |
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| author | Rami Al-Ruzouq Mohamed Barakat A. Gibril Abdallah Shanableh Abubakir Kais Osman Hamed Saeed Al-Mansoori Mohamad Ali Khalil |
| author_facet | Rami Al-Ruzouq Mohamed Barakat A. Gibril Abdallah Shanableh Abubakir Kais Osman Hamed Saeed Al-Mansoori Mohamad Ali Khalil |
| author_sort | Rami Al-Ruzouq |
| collection | DOAJ |
| description | Remote sensing technologies and machine learning (ML) algorithms play an increasingly important role in accurate detection and monitoring of oil spill slicks, assisting scientists in forecasting their trajectories, developing clean-up plans, taking timely and urgent actions, and applying effective treatments to contain and alleviate adverse effects. Review and analysis of different sources of remotely sensed data and various components of ML classification systems for oil spill detection and monitoring are presented in this study. More than 100 publications in the field of oil spill remote sensing, published in the past 10 years, are reviewed in this paper. The first part of this review discusses the strengths and weaknesses of different sources of remotely sensed data used for oil spill detection. Necessary preprocessing and preparation of data for developing classification models are then highlighted. Feature extraction, feature selection, and widely used handcrafted features for oil spill detection are subsequently introduced and analyzed. The second part of this review explains the use and capabilities of different classical and developed state-of-the-art ML techniques for oil spill detection. Finally, an in-depth discussion on limitations, open challenges, considerations of oil spill classification systems using remote sensing, and state-of-the-art ML algorithms are highlighted along with conclusions and insights into future directions. |
| first_indexed | 2024-03-10T15:40:29Z |
| format | Article |
| id | doaj.art-6f4c8dbb7d674f67a03cd0ddc993abbf |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T15:40:29Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-6f4c8dbb7d674f67a03cd0ddc993abbf2023-11-20T16:54:42ZengMDPI AGRemote Sensing2072-42922020-10-011220333810.3390/rs12203338Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A ReviewRami Al-Ruzouq0Mohamed Barakat A. Gibril1Abdallah Shanableh2Abubakir Kais3Osman Hamed4Saeed Al-Mansoori5Mohamad Ali Khalil6Civil and Environmental Engineering Department, University of Sharjah, Sharjah 27272, UAEGIS & Remote Sensing Center, Research Institute of Sciences and Engineering, University of Sharjah, Sharjah 27272, UAECivil and Environmental Engineering Department, University of Sharjah, Sharjah 27272, UAECivil and Environmental Engineering Department, University of Sharjah, Sharjah 27272, UAEFaculty of Science and Engineering, University of Wolverhampton, Wolverhampton WV1 1LY, UKApplications Development and Analysis Section (ADAS), Mohammed Bin Rashid Space Centre (MBRSC), Dubai 211833, UAEGIS & Remote Sensing Center, Research Institute of Sciences and Engineering, University of Sharjah, Sharjah 27272, UAERemote sensing technologies and machine learning (ML) algorithms play an increasingly important role in accurate detection and monitoring of oil spill slicks, assisting scientists in forecasting their trajectories, developing clean-up plans, taking timely and urgent actions, and applying effective treatments to contain and alleviate adverse effects. Review and analysis of different sources of remotely sensed data and various components of ML classification systems for oil spill detection and monitoring are presented in this study. More than 100 publications in the field of oil spill remote sensing, published in the past 10 years, are reviewed in this paper. The first part of this review discusses the strengths and weaknesses of different sources of remotely sensed data used for oil spill detection. Necessary preprocessing and preparation of data for developing classification models are then highlighted. Feature extraction, feature selection, and widely used handcrafted features for oil spill detection are subsequently introduced and analyzed. The second part of this review explains the use and capabilities of different classical and developed state-of-the-art ML techniques for oil spill detection. Finally, an in-depth discussion on limitations, open challenges, considerations of oil spill classification systems using remote sensing, and state-of-the-art ML algorithms are highlighted along with conclusions and insights into future directions.https://www.mdpi.com/2072-4292/12/20/3338marine pollutionoil spill remote sensingoil spill detectionSARdark spot detectionfeature extraction |
| spellingShingle | Rami Al-Ruzouq Mohamed Barakat A. Gibril Abdallah Shanableh Abubakir Kais Osman Hamed Saeed Al-Mansoori Mohamad Ali Khalil Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review Remote Sensing marine pollution oil spill remote sensing oil spill detection SAR dark spot detection feature extraction |
| title | Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review |
| title_full | Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review |
| title_fullStr | Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review |
| title_full_unstemmed | Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review |
| title_short | Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review |
| title_sort | sensors features and machine learning for oil spill detection and monitoring a review |
| topic | marine pollution oil spill remote sensing oil spill detection SAR dark spot detection feature extraction |
| url | https://www.mdpi.com/2072-4292/12/20/3338 |
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