Decision Fusion of Deep Learning and Shallow Learning for Marine Oil Spill Detection
Marine oil spills are an emergency of great harm and have become a hot topic in marine environmental monitoring research. Optical remote sensing is an important means to monitor marine oil spills. Clouds, weather, and light control the amount of available data, which often limit feature characteriza...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/3/666 |
| _version_ | 1827658983936098304 |
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| author | Junfang Yang Yi Ma Yabin Hu Zongchen Jiang Jie Zhang Jianhua Wan Zhongwei Li |
| author_facet | Junfang Yang Yi Ma Yabin Hu Zongchen Jiang Jie Zhang Jianhua Wan Zhongwei Li |
| author_sort | Junfang Yang |
| collection | DOAJ |
| description | Marine oil spills are an emergency of great harm and have become a hot topic in marine environmental monitoring research. Optical remote sensing is an important means to monitor marine oil spills. Clouds, weather, and light control the amount of available data, which often limit feature characterization using a single classifier and therefore difficult to accurate monitoring of marine oil spills. In this paper, we develop a decision fusion algorithm to integrate deep learning methods and shallow learning methods based on multi-scale features for improving oil spill detection accuracy in the case of limited samples. Based on the multi-scale features after wavelet transform, two deep learning methods and two classical shallow learning algorithms are used to extract oil slick information from hyperspectral oil spill images. The decision fusion algorithm based on fuzzy membership degree is introduced to fuse multi-source oil spill information. The research shows that oil spill detection accuracy using the decision fusion algorithm is higher than that of the single detection algorithms. It is worth noting that oil spill detection accuracy is affected by different scale features. The decision fusion algorithm under the first-level scale features can further improve the accuracy of oil spill detection. The overall classification accuracy of the proposed method is 91.93%, which is 2.03%, 2.15%, 1.32%, and 0.43% higher than that of SVM, DBN, 1D-CNN, and MRF-CNN algorithms, respectively. |
| first_indexed | 2024-03-09T23:13:13Z |
| format | Article |
| id | doaj.art-edb07b93b3184754a973e6e47956fcda |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T23:13:13Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-edb07b93b3184754a973e6e47956fcda2023-11-23T17:41:31ZengMDPI AGRemote Sensing2072-42922022-01-0114366610.3390/rs14030666Decision Fusion of Deep Learning and Shallow Learning for Marine Oil Spill DetectionJunfang Yang0Yi Ma1Yabin Hu2Zongchen Jiang3Jie Zhang4Jianhua Wan5Zhongwei Li6College of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, ChinaFirst Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, ChinaFirst Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, ChinaFirst Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, ChinaCollege of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, ChinaCollege of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, ChinaCollege of Oceanography and Space Informatics, China University of Petroleum, Qingdao 266580, ChinaMarine oil spills are an emergency of great harm and have become a hot topic in marine environmental monitoring research. Optical remote sensing is an important means to monitor marine oil spills. Clouds, weather, and light control the amount of available data, which often limit feature characterization using a single classifier and therefore difficult to accurate monitoring of marine oil spills. In this paper, we develop a decision fusion algorithm to integrate deep learning methods and shallow learning methods based on multi-scale features for improving oil spill detection accuracy in the case of limited samples. Based on the multi-scale features after wavelet transform, two deep learning methods and two classical shallow learning algorithms are used to extract oil slick information from hyperspectral oil spill images. The decision fusion algorithm based on fuzzy membership degree is introduced to fuse multi-source oil spill information. The research shows that oil spill detection accuracy using the decision fusion algorithm is higher than that of the single detection algorithms. It is worth noting that oil spill detection accuracy is affected by different scale features. The decision fusion algorithm under the first-level scale features can further improve the accuracy of oil spill detection. The overall classification accuracy of the proposed method is 91.93%, which is 2.03%, 2.15%, 1.32%, and 0.43% higher than that of SVM, DBN, 1D-CNN, and MRF-CNN algorithms, respectively.https://www.mdpi.com/2072-4292/14/3/666oil spill detectiondecision fusionconvolutional neural networkdeep belief networkshallow learninghyperspectral image |
| spellingShingle | Junfang Yang Yi Ma Yabin Hu Zongchen Jiang Jie Zhang Jianhua Wan Zhongwei Li Decision Fusion of Deep Learning and Shallow Learning for Marine Oil Spill Detection Remote Sensing oil spill detection decision fusion convolutional neural network deep belief network shallow learning hyperspectral image |
| title | Decision Fusion of Deep Learning and Shallow Learning for Marine Oil Spill Detection |
| title_full | Decision Fusion of Deep Learning and Shallow Learning for Marine Oil Spill Detection |
| title_fullStr | Decision Fusion of Deep Learning and Shallow Learning for Marine Oil Spill Detection |
| title_full_unstemmed | Decision Fusion of Deep Learning and Shallow Learning for Marine Oil Spill Detection |
| title_short | Decision Fusion of Deep Learning and Shallow Learning for Marine Oil Spill Detection |
| title_sort | decision fusion of deep learning and shallow learning for marine oil spill detection |
| topic | oil spill detection decision fusion convolutional neural network deep belief network shallow learning hyperspectral image |
| url | https://www.mdpi.com/2072-4292/14/3/666 |
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