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Application of Deep Networks to Oil Spill Detection Using Polarimetric Synthetic Aperture Radar Images
Chen, Guandong1; Li, Yu1; Sun, Guangmin1; Zhang, Yuanzhi2,3
2017-10-01
Source PublicationAPPLIED SCIENCES-BASEL
Volume7Issue:10
AbstractPolarimetric synthetic aperture radar (SAR) remote sensing provides an outstanding tool in oil spill detection and classification, for its advantages in distinguishing mineral oil and biogenic lookalikes. Various features can be extracted from polarimetric SAR data. The large number and correlated nature of polarimetric SAR features make the selection and optimization of these features impact on the performance of oil spill classification algorithms. In this paper, deep learning algorithms such as the stacked autoencoder (SAE) and deep belief network (DBN) are applied to optimize the polarimetric feature sets and reduce the feature dimension through layer-wise unsupervised pre-training. An experiment was conducted on RADARSAT-2 quad-polarimetric SAR image acquired during the Norwegian oil-on-water exercise of 2011, in which verified mineral, emulsions, and biogenic slicks were analyzed. The results show that oil spill classification achieved by deep networks outperformed both support vector machine (SVM) and traditional artificial neural networks (ANN) with similar parameter settings, especially when the number of training data samples is limited.
SubtypeArticle
KeywordOil Spill Polarimetric Synthetic Aperture Radar (Sar) Deep Belief Network Autoencoder Remote Sensing
WOS HeadingsScience & Technology ; Physical Sciences ; Technology
Funding OrganizationNational Key Research and Development Program of China(2016YFB0501501) ; National Key Research and Development Program of China(2016YFB0501501) ; Natural Scientific Foundation of China(41471353 ; Natural Scientific Foundation of China(41471353 ; 41706201) ; 41706201) ; National Key Research and Development Program of China(2016YFB0501501) ; National Key Research and Development Program of China(2016YFB0501501) ; Natural Scientific Foundation of China(41471353 ; Natural Scientific Foundation of China(41471353 ; 41706201) ; 41706201)
DOI10.3390/app7100968
WOS KeywordMEDITERRANEAN SEA ; NEURAL-NETWORKS ; SAR ; ALGORITHM
Indexed BySCI
Language英语
Funding OrganizationNational Key Research and Development Program of China(2016YFB0501501) ; National Key Research and Development Program of China(2016YFB0501501) ; Natural Scientific Foundation of China(41471353 ; Natural Scientific Foundation of China(41471353 ; 41706201) ; 41706201) ; National Key Research and Development Program of China(2016YFB0501501) ; National Key Research and Development Program of China(2016YFB0501501) ; Natural Scientific Foundation of China(41471353 ; Natural Scientific Foundation of China(41471353 ; 41706201) ; 41706201)
WOS Research AreaChemistry ; Materials Science ; Physics
WOS SubjectChemistry, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS IDWOS:000414457800006
Citation statistics
Document Type期刊论文
Identifierhttp://ir.bao.ac.cn/handle/114a11/20216
Collection月球与深空探测研究部
Affiliation1.Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
2.Chinese Acad Sci, Natl Astron Observ, Beijing 100012, Peoples R China
3.Chinese Acad Sci, Key Lab Lunar Sci & Deep Space Explorat, Beijing 100012, Peoples R China
Recommended Citation
GB/T 7714
Chen, Guandong,Li, Yu,Sun, Guangmin,et al. Application of Deep Networks to Oil Spill Detection Using Polarimetric Synthetic Aperture Radar Images[J]. APPLIED SCIENCES-BASEL,2017,7(10).
APA Chen, Guandong,Li, Yu,Sun, Guangmin,&Zhang, Yuanzhi.(2017).Application of Deep Networks to Oil Spill Detection Using Polarimetric Synthetic Aperture Radar Images.APPLIED SCIENCES-BASEL,7(10).
MLA Chen, Guandong,et al."Application of Deep Networks to Oil Spill Detection Using Polarimetric Synthetic Aperture Radar Images".APPLIED SCIENCES-BASEL 7.10(2017).
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