Institutional Repository of Key Laboratory of Ocean Circulation and Wave Studies, Institute of Oceanology, Chinese Academy of Sciences
| A Novel Marine Oil Spillage Identification Scheme Based on Convolution Neural Network Feature Extraction From Fully Polarimetric SAR Imagery | |
| Song, Dongmei1,2; Zhen, Zongjin1,3; Wang, Bin1,2; Li, Xiaofeng4; Gao, Le5; Wang, Ning6; Xie, Tao7; Zhang, Ting8 | |
| 2020 | |
| Source Publication | IEEE ACCESS
 |
| ISSN | 2169-3536 |
| Volume | 8Pages:59801-59820 |
| Corresponding Author | Song, Dongmei(songdongmei@upc.edu.cn) ; Zhen, Zongjin(zhenzongjin@126.com) ; Wang, Bin(wangbin007@upc.edu.cn) ; Li, Xiaofeng(lixf@qdio.ac.cn) ; Wang, Ning(wangning1087@163.com) |
| Abstract | 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. |
| Keyword | Marine oil spill RADARSAT-2 PolSAR deep learning feature extraction convolutional neural network (CNN) |
| DOI | 10.1109/ACCESS.2020.2979219 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Key Research and Development Program of China[2017YFC1405600] ; National Science Foundation of China[41772350] ; National Science Foundation of China[41701513] ; National Science Foundation of China[61371189] ; National Science Foundation of China[41706208] ; National Science Foundation of China[41576032] ; National Science Foundation of China[41776181] ; Key Program of Joint Fund of the National Natural Science Foundation of China[U1906217] ; Shandong Province[U1906217] ; Key Research and Development Program of Shandong province[2019GGX101033] ; Fundamental Research Funds for the Central Universities[19CX05003A-8] |
| WOS Research Area | Computer Science ; Engineering ; Telecommunications |
| WOS Subject | Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications |
| WOS ID | WOS:000527413100014 |
| Publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.qdio.ac.cn/handle/337002/167266 |
| Collection | 海洋环流与波动重点实验室 |
| Corresponding Author | Song, Dongmei; Zhen, Zongjin; Wang, Bin; Li, Xiaofeng; Wang, Ning |
| Affiliation | 1.China Univ Petr East China, Coll Oceanog & Space Informat, Qingdao 266580, Peoples R China 2.Qingdao Natl Lab Marine Sci & Technol, Lab Marine Mineral Resources, Qingdao 266071, Peoples R China 3.China Univ Petr, Grad Sch, Qingdao 266580, Peoples R China 4.Chinese Acad Sci, Inst Oceanol, CAS Key Lab Ocean Circulat & Waves, Qingdao 266071, Peoples R China 5.Chinese Acad Sci, Ctr Ocean Mega Sci, Qingdao 266071, Peoples R China 6.State Ocean Adm, North China Sea Marine Forecasting Ctr, Qingdao 266061, Peoples R China 7.Nanjing Univ Informat Sci & Technol, Sch Remote Sensing & Geomat Engn, Nanjing 210044, Peoples R China 8.Minist Nat Resources, Inst Oceanog 1, Qingdao 266061, Peoples R China |
| Corresponding Author Affilication | Institute of Oceanology, Chinese Academy of Sciences |
| Recommended Citation GB/T 7714 | Song, Dongmei,Zhen, Zongjin,Wang, Bin,et al. A Novel Marine Oil Spillage Identification Scheme Based on Convolution Neural Network Feature Extraction From Fully Polarimetric SAR Imagery[J]. IEEE ACCESS,2020,8:59801-59820. |
| APA | Song, Dongmei.,Zhen, Zongjin.,Wang, Bin.,Li, Xiaofeng.,Gao, Le.,...&Zhang, Ting.(2020).A Novel Marine Oil Spillage Identification Scheme Based on Convolution Neural Network Feature Extraction From Fully Polarimetric SAR Imagery.IEEE ACCESS,8,59801-59820. |
| MLA | Song, Dongmei,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. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| A Novel Marine Oil S（5827KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View | |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment