IOCAS-IR  > 海洋环流与波动重点实验室
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 PublicationIEEE ACCESS
ISSN2169-3536
Volume8Pages:59801-59820
Corresponding AuthorSong, 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)
AbstractMarine 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.
KeywordMarine oil spill RADARSAT-2 PolSAR deep learning feature extraction convolutional neural network (CNN)
DOI10.1109/ACCESS.2020.2979219
Indexed BySCI
Language英语
Funding ProjectNational 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 AreaComputer Science ; Engineering ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS IDWOS:000527413100014
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.qdio.ac.cn/handle/337002/167266
Collection海洋环流与波动重点实验室
Corresponding AuthorSong, Dongmei; Zhen, Zongjin; Wang, Bin; Li, Xiaofeng; Wang, Ning
Affiliation1.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 AffilicationInstitute 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.
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