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Remote Sensing Technology and Application  2013, Vol. 28 Issue (6): 1013-1019    DOI:
    
Compressive Sensing Spectral Sparsification Method based on Training Dictionary
Wang Qi1,2,Li Chuanrong1,Ma Lingling1,Tang Lingli1,Li Jianjian1,2
(1.Key Laboratory of Quantitative Remote Sensing Information Technology,Academy of Opto\|Electronics,Chinese Academy of Sciences,Beijing 100094,China;
2.University of Chinese Academy of Sciences,Beijing 100049,China)
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Abstract  

Using object’s sparsity,compressive sensing theory is able to breakthrough Nyquist sampling theorem and reconstruct object image from very few observations,which has become a very potential research field to achieve super-resolution image.One of the crucial problems of applying compressive sensing to earth observation remote sensing image is how to pursue effective sparsification method under the complexity of ground scene.It will be more practical for hyperspectral imaging with a large amount of data to search for effective spectral sparsification method using rich redundant spectral information in bands.This paper introduces the basic principle of compressive sensing spectral imaging and spectral sparsification,and trains a redundant dictionary based on K\|SVD method using multiple types of ground objects’ spectral data from ASTER spectral library,reconstructs several typical objects using DCT basis,wavelet basis and training dictionary via simulation experiment and compares the reconstruction results.The result shows using training dictionary as sparse basis performs is better at low sampling rate than DCT basis and wavelet basis,spectral curves can be reconstructed almost perfectly at sampling rate as low as 20%.Our research indicates the great potential for sparse dictionary applied in compressive sensing spectral imaging.

Key words:  Compressive sensing      Hyperspectral imaging      Sparse basis      Dictionary training     
Received:  29 November 2012      Published:  28 February 2014
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Wang Qi
Li Chuanrong
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Li Jianjian

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Wang Qi,Li Chuanrong,Ma Lingling,Tang Lingli,Li Jianjian. Compressive Sensing Spectral Sparsification Method based on Training Dictionary. Remote Sensing Technology and Application, 2013, 28(6): 1013-1019.

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http://www.rsta.ac.cn/EN/     OR     http://www.rsta.ac.cn/EN/Y2013/V28/I6/1013

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