A Novel Marine Oil Spillage Identification Scheme Based on Convolution Neural Network Feature Extraction From Fully Polarimetric SAR Imagery
Marine oil spill pollution has caused serious impacts on marine ecological environments, ecological resources and marine economy. Synthetic Aperture Radar (SAR), especially polarmetric SAR (PolSAR), has been proven to be a powerful and efficient tool for marine oil spill detection. In general, tradi...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9050656/ |
| _version_ | 1818857089392443392 |
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| author | Dongmei Song Zongjin Zhen Bin Wang Xiaofeng Li Le Gao Ning Wang Tao Xie Ting Zhang |
| author_facet | Dongmei Song Zongjin Zhen Bin Wang Xiaofeng Li Le Gao Ning Wang Tao Xie Ting Zhang |
| author_sort | Dongmei Song |
| collection | DOAJ |
| description | Marine oil spill pollution has caused serious impacts on marine ecological environments, ecological resources and marine economy. Synthetic Aperture Radar (SAR), especially polarmetric SAR (PolSAR), has been proven to be a powerful and efficient tool for marine oil spill detection. In general, traditional oil spill detection methods mainly rely on artificially-extracted polarization characteristics, and the detection accuracy is limited by the quality of feature extraction. Recently proposed Convolutional neural network (CNN) is capable of mining spatial feature from large data set automatically. Inspired by these, in this paper we proposed a novel oil spill identification method based on multi-layer deep feature extraction by CNN. Firstly, PolSAR data are converted into a 9-channel data block to feed the CNN. Then, a 5-layer CNN architecture is built to extract two high-level features from the original data automatically. The features are fused after dimension reduction via principal component analysis (PCA). Finally, support vector machine method with radial basis function kernel (RBF-SVM) is utilized for classification. Three sets of RADARSAT-2 fully polarimetric SAR data were used in this study to validate the proposed method. The obtained results reveal that the proposed method provides competitive results in overall classification accuracy and kappa coefficient. Moreover, this method can improve the accuracy of oil spill detection, reduce the false alarm rate, and effectively distinguish an oil spill from a biogenic slick. |
| first_indexed | 2024-12-19T08:34:51Z |
| format | Article |
| id | doaj.art-cdb804688fbd4611a4edcf1489efacf4 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T08:34:51Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cdb804688fbd4611a4edcf1489efacf42022-12-21T20:29:04ZengIEEEIEEE Access2169-35362020-01-018598015982010.1109/ACCESS.2020.29792199050656A Novel Marine Oil Spillage Identification Scheme Based on Convolution Neural Network Feature Extraction From Fully Polarimetric SAR ImageryDongmei Song0https://orcid.org/0000-0002-2420-8012Zongjin Zhen1Bin Wang2https://orcid.org/0000-0003-2565-1013Xiaofeng Li3Le Gao4Ning Wang5Tao Xie6Ting Zhang7College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, ChinaCollege of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, ChinaCollege of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao, ChinaCAS Key Laboratory of Ocean Circulation and Waves, Institute of Oceanology, Chinese Academy of Sciences, Qingdao, ChinaCenter for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao, ChinaNorth China Sea Marine Forecasting Center, State Oceanic Administration, Qingdao, ChinaSchool of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing, ChinaMinistry of Natural Resources, First Institute of Oceanography, Qingdao, ChinaMarine oil spill pollution has caused serious impacts on marine ecological environments, ecological resources and marine economy. Synthetic Aperture Radar (SAR), especially polarmetric SAR (PolSAR), has been proven to be a powerful and efficient tool for marine oil spill detection. In general, traditional oil spill detection methods mainly rely on artificially-extracted polarization characteristics, and the detection accuracy is limited by the quality of feature extraction. Recently proposed Convolutional neural network (CNN) is capable of mining spatial feature from large data set automatically. Inspired by these, in this paper we proposed a novel oil spill identification method based on multi-layer deep feature extraction by CNN. Firstly, PolSAR data are converted into a 9-channel data block to feed the CNN. Then, a 5-layer CNN architecture is built to extract two high-level features from the original data automatically. The features are fused after dimension reduction via principal component analysis (PCA). Finally, support vector machine method with radial basis function kernel (RBF-SVM) is utilized for classification. Three sets of RADARSAT-2 fully polarimetric SAR data were used in this study to validate the proposed method. The obtained results reveal that the proposed method provides competitive results in overall classification accuracy and kappa coefficient. Moreover, this method can improve the accuracy of oil spill detection, reduce the false alarm rate, and effectively distinguish an oil spill from a biogenic slick.https://ieeexplore.ieee.org/document/9050656/Marine oil spillRADARSAT-2PolSARdeep learningfeature extractionconvolutional neural network (CNN) |
| spellingShingle | Dongmei Song Zongjin Zhen Bin Wang Xiaofeng Li Le Gao Ning Wang Tao Xie Ting Zhang A Novel Marine Oil Spillage Identification Scheme Based on Convolution Neural Network Feature Extraction From Fully Polarimetric SAR Imagery IEEE Access Marine oil spill RADARSAT-2 PolSAR deep learning feature extraction convolutional neural network (CNN) |
| title | A Novel Marine Oil Spillage Identification Scheme Based on Convolution Neural Network Feature Extraction From Fully Polarimetric SAR Imagery |
| title_full | A Novel Marine Oil Spillage Identification Scheme Based on Convolution Neural Network Feature Extraction From Fully Polarimetric SAR Imagery |
| title_fullStr | A Novel Marine Oil Spillage Identification Scheme Based on Convolution Neural Network Feature Extraction From Fully Polarimetric SAR Imagery |
| title_full_unstemmed | A Novel Marine Oil Spillage Identification Scheme Based on Convolution Neural Network Feature Extraction From Fully Polarimetric SAR Imagery |
| title_short | A Novel Marine Oil Spillage Identification Scheme Based on Convolution Neural Network Feature Extraction From Fully Polarimetric SAR Imagery |
| title_sort | novel marine oil spillage identification scheme based on convolution neural network feature extraction from fully polarimetric sar imagery |
| topic | Marine oil spill RADARSAT-2 PolSAR deep learning feature extraction convolutional neural network (CNN) |
| url | https://ieeexplore.ieee.org/document/9050656/ |
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