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 | |
| 发表期刊 | IEEE ACCESS
 |
| ISSN | 2169-3536 |
| 卷号 | 8页码:59801-59820 |
| 通讯作者 | 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) |
| 摘要 | 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. |
| 关键词 | Marine oil spill RADARSAT-2 PolSAR deep learning feature extraction convolutional neural network (CNN) |
| DOI | 10.1109/ACCESS.2020.2979219 |
| 收录类别 | SCI |
| 语种 | 英语 |
| 资助项目 | 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研究方向 | Computer Science ; Engineering ; Telecommunications |
| WOS类目 | Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications |
| WOS记录号 | WOS:000527413100014 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.qdio.ac.cn/handle/337002/167266 |
| 专题 | 海洋环流与波动重点实验室 |
| 通讯作者 | Song, Dongmei; Zhen, Zongjin; Wang, Bin; Li, Xiaofeng; Wang, Ning |
| 作者单位 | 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 |
| 通讯作者单位 | 中国科学院海洋研究所 |
| 推荐引用方式 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. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| A Novel Marine Oil S（5827KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 浏览 | |
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