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Remote Sensing Technology and Application  2007, Vol. 22 Issue (6): 739-742    DOI: 10.11873/j.issn.1004-0323.2007.6.739
    
Lossless Compression of Hyperspectral Image Based on Adaptive Prediction
KUANG Jun1,LUO Jian-shu1,XIANG Lu2
1. Academy of Sciences,National University of Defense and Technology,Changsha 410073,China;
2.Electronic Technology Institute of Information Engineering University,Guangzhou 510510,China
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Abstract  

Hyperspectral images are hard to compress because of their abundant details,complicated texture and insignificant special correlation.Making use of the significant spectral correlation within the hyperspectral images,We use pixels of several bands to adaptively predict the pixels of the current band.We can know the optimal prediction of the current pixels is its conditional expectation,which can be translated into computational expression by using subsection ingtegral ,and associate with pixels of other bands.We choose neighboring pixels to adaptive estimate every parameter in order to get residual image,which remove most spatial and spectral redundancy,and then we use JPEG-LS to remove the spectral redundancy. Experiments show that the algorithm can compress the data efficiently and works better than other algorithms.

Key words:  Hyperspectral image,Lossless compression      Conditional expectation,Adaptive prediction      JPEG-LS     
Received:  14 May 2007      Published:  03 September 2010
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KUANG Jun
LUO Jian-Shu
XIANG Lu

Cite this article: 

KUANG Jun, LUO Jian-Shu, XIANG Lu. Lossless Compression of Hyperspectral Image Based on Adaptive Prediction. Remote Sensing Technology and Application, 2007, 22(6): 739-742.

URL: 

http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2007.6.739     OR     http://www.rsta.ac.cn/EN/Y2007/V22/I6/739

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