Offshore Oil Spill Detection Based on CNN, DBSCAN, and Hyperspectral Imaging
Offshore oil spills have the potential to inflict substantial ecological damage, underscoring the critical importance of timely offshore oil spill detection and remediation. At present, offshore oil spill detection typically combines hyperspectral imaging with deep learning techniques. While these m...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/2/411 |
| _version_ | 1797339409664704512 |
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| author | Ce Zhan Kai Bai Binrui Tu Wanxing Zhang |
| author_facet | Ce Zhan Kai Bai Binrui Tu Wanxing Zhang |
| author_sort | Ce Zhan |
| collection | DOAJ |
| description | Offshore oil spills have the potential to inflict substantial ecological damage, underscoring the critical importance of timely offshore oil spill detection and remediation. At present, offshore oil spill detection typically combines hyperspectral imaging with deep learning techniques. While these methodologies have made significant advancements, they prove inadequate in scenarios requiring real-time detection due to limited model detection speeds. To address this challenge, a method for detecting oil spill areas is introduced, combining convolutional neural networks (CNNs) with the DBSCAN clustering algorithm. This method aims to enhance the efficiency of oil spill area detection in real-time scenarios, providing a potential solution to the limitations posed by the intricate structures of existing models. The proposed method includes a pre-feature selection process applied to the spectral data, followed by pixel classification using a convolutional neural network (CNN) model. Subsequently, the DBSCAN algorithm is employed to segment oil spill areas from the classification results. To validate our proposed method, we simulate an offshore oil spill environment in the laboratory, utilizing a hyperspectral sensing device to collect data and create a dataset. We then compare our method with three other models—DRSNet, CNN-Visual Transformer, and GCN—conducting a comprehensive analysis to evaluate the advantages and limitations of each model. |
| first_indexed | 2024-03-08T09:47:43Z |
| format | Article |
| id | doaj.art-5e1c2759eafb473ca12f2c93c5c5e764 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T09:47:43Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-5e1c2759eafb473ca12f2c93c5c5e7642024-01-29T14:14:24ZengMDPI AGSensors1424-82202024-01-0124241110.3390/s24020411Offshore Oil Spill Detection Based on CNN, DBSCAN, and Hyperspectral ImagingCe Zhan0Kai Bai1Binrui Tu2Wanxing Zhang3Hubei Key Laboratory of Drilling and Production Engineering for Oil and Gas, Yangtze University, Jingzhou 430023, ChinaHubei Key Laboratory of Drilling and Production Engineering for Oil and Gas, Yangtze University, Jingzhou 430023, ChinaHubei Key Laboratory of Drilling and Production Engineering for Oil and Gas, Yangtze University, Jingzhou 430023, ChinaHubei Key Laboratory of Drilling and Production Engineering for Oil and Gas, Yangtze University, Jingzhou 430023, ChinaOffshore oil spills have the potential to inflict substantial ecological damage, underscoring the critical importance of timely offshore oil spill detection and remediation. At present, offshore oil spill detection typically combines hyperspectral imaging with deep learning techniques. While these methodologies have made significant advancements, they prove inadequate in scenarios requiring real-time detection due to limited model detection speeds. To address this challenge, a method for detecting oil spill areas is introduced, combining convolutional neural networks (CNNs) with the DBSCAN clustering algorithm. This method aims to enhance the efficiency of oil spill area detection in real-time scenarios, providing a potential solution to the limitations posed by the intricate structures of existing models. The proposed method includes a pre-feature selection process applied to the spectral data, followed by pixel classification using a convolutional neural network (CNN) model. Subsequently, the DBSCAN algorithm is employed to segment oil spill areas from the classification results. To validate our proposed method, we simulate an offshore oil spill environment in the laboratory, utilizing a hyperspectral sensing device to collect data and create a dataset. We then compare our method with three other models—DRSNet, CNN-Visual Transformer, and GCN—conducting a comprehensive analysis to evaluate the advantages and limitations of each model.https://www.mdpi.com/1424-8220/24/2/411offshore oil spillartificial neural networkhyperspectral image |
| spellingShingle | Ce Zhan Kai Bai Binrui Tu Wanxing Zhang Offshore Oil Spill Detection Based on CNN, DBSCAN, and Hyperspectral Imaging Sensors offshore oil spill artificial neural network hyperspectral image |
| title | Offshore Oil Spill Detection Based on CNN, DBSCAN, and Hyperspectral Imaging |
| title_full | Offshore Oil Spill Detection Based on CNN, DBSCAN, and Hyperspectral Imaging |
| title_fullStr | Offshore Oil Spill Detection Based on CNN, DBSCAN, and Hyperspectral Imaging |
| title_full_unstemmed | Offshore Oil Spill Detection Based on CNN, DBSCAN, and Hyperspectral Imaging |
| title_short | Offshore Oil Spill Detection Based on CNN, DBSCAN, and Hyperspectral Imaging |
| title_sort | offshore oil spill detection based on cnn dbscan and hyperspectral imaging |
| topic | offshore oil spill artificial neural network hyperspectral image |
| url | https://www.mdpi.com/1424-8220/24/2/411 |
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