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Remote Sensing Technology and Application  2010, Vol. 25 Issue (2): 221-226    DOI: 10.11873/j.issn.1004-0323.2010.2.221
article     
Feature Extraction of Offshore Oil Slick from Hyperspectral Data Based on EMD and Neural Network
DONG Shi-wei1,ZHOU Zi-yong1,WEN Bai-hong2
1.State Key Laboratory of Petroleum Resource and Prospecting, China University of Petroleum,Beijing 102200,China;
2.Research Institute of Petroleum Exploration and Development,CNPC,Beijing 100083,China
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Abstract  

Based on the reflectance spectrum of oil slick and background seawater,an EMD based approach to feature extraction from hyperspectral data is presented in the work,and a BP neural network is constructed to classify the oil slick and seawater samples.Firstly,the EMD is adopted to decompose the hyperspectral data from 425 nm to 2 390 nm into a series of Intrinsic Mode Functions ( IMFs) ,and the energy of each IMFs is computed,then based on the computed energy,some IMFs energy are selected as inputed parameter of neural networks to classify the oil slick and seawater.The results show that the EMD based feature extraction approach can identify oil slick accurately and effectively.

Key words:   Empirical mode decomposition      Neural network      Oil slick      Hyperspectral      Feature extraction     
Received:  06 July 2009      Published:  19 October 2010
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DONG Shi-wei
ZHOU Zi-yong
WEN Bai-hong

Cite this article: 

DONG Shi-wei, ZHOU Zi-yong, WEN Bai-hong. Feature Extraction of Offshore Oil Slick from Hyperspectral Data Based on EMD and Neural Network. Remote Sensing Technology and Application, 2010, 25(2): 221-226.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2010.2.221     OR     http://www.rsta.ac.cn/EN/Y2010/V25/I2/221

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