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Application of Deep Networks to Oil Spill Detection Using Polarimetric Synthetic Aperture Radar Images

文献类型：期刊论文

作者Li, Yu3; Chen, Guandong3; Sun, Guangmin3; Zhang, Yuanzhi1,2
刊名APPLIED SCIENCES-BASEL
出版日期2017-10-01
卷号7期号:10
关键词Oil Spill Polarimetric Synthetic Aperture Radar (Sar) Deep Belief Network Autoencoder Remote Sensing
DOI10.3390/app7100968
文献子类Article
英文摘要Polarimetric 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.
WOS关键词MEDITERRANEAN SEA ; NEURAL-NETWORKS ; SAR ; ALGORITHM
WOS研究方向Chemistry ; Materials Science ; Physics
语种英语
WOS记录号WOS:000414457800006
资助机构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) ; 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)
源URL[http://ir.bao.ac.cn/handle/114a11/20216]  
专题国家天文台_月球与深空探测研究部
作者单位1.Chinese Acad Sci, Key Lab Lunar Sci & Deep Space Explorat, Beijing 100012, Peoples R China
2.Chinese Acad Sci, Natl Astron Observ, Beijing 100012, Peoples R China
3.Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
推荐引用方式
GB/T 7714		 Li, Yu,Chen, Guandong,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 Li, Yu,Chen, Guandong,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 Li, Yu,et al."Application of Deep Networks to Oil Spill Detection Using Polarimetric Synthetic Aperture Radar Images".APPLIED SCIENCES-BASEL 7.10(2017).

入库方式： OAI收割

来源：国家天文台

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