Hyperspectral compressive sensing reconstruction based on spectral sparse model

Home > Archive>Volume 35, Issue 6, 2016 >723-730

Hyperspectral compressive sensing reconstruction based on spectral sparse model
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                        WANG QiWANG Qi
Key Laboratory of Quantitative Remote Sensing Information Technology，Academy of Opto-Electronics, Chinese Academy of Sciences
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MA Ling-LingMA Ling-Ling
Key Laboratory of Quantitative Remote Sensing Information Technology，Academy of Opto-Electronics, Chinese Academy of Sciences
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TANG Ling-LiTANG Ling-Li
Academy of Opto-Electronics, Chinese Academy of Sciences
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LI Chuan-RongLI Chuan-Rong
Academy of Opto-Electronics, Chinese Academy of Sciences
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ZHOU Yong-ShengZHOU Yong-Sheng
Academy of Opto-Electronics, Chinese Academy of Sciences
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Affiliation:Key Laboratory of Quantitative Remote Sensing Information Technology，Academy of Opto-Electronics, Chinese Academy of Sciences,Key Laboratory of Quantitative Remote Sensing Information Technology，Academy of Opto-Electronics, Chinese Academy of Sciences,Academy of Opto-Electronics, Chinese Academy of Sciences,Academy of Opto-Electronics, Chinese Academy of Sciences,Academy of Opto-Electronics, Chinese Academy of Sciences
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Abstract:

A new compressive sensing(CS) sampling and reconstruction model based on spectral sparse representation is put forward in this paper. The spectral sparse dictionary is constructed from training samples to enhance the effect of sparse representation and the total variation restriction of spatial images is also considered to further enhance the precision during the reconstruction. The experiment to reconstruct 200 bands AVIRIS hyperspectral images show that the effect of spectral sparse representation enhances largely compared with traditional DCT dictionary and Haar wavelet dictionary, and the hyperspectral image is reconstructed nearly perfectly at 25% sampling rate and the spatial and spectral precision is higher than existing common methods in the same condition.

Key words:compressive sensing(CS), hyperspectral imaging, sparse representation, dictionary learning, reconstruction algorithm

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WANG Qi, MA Ling-Ling, TANG Ling-Li, LI Chuan-Rong, ZHOU Yong-Sheng. Hyperspectral compressive sensing reconstruction based on spectral sparse model[J]. Journal of Infrared and Millimeter Waves,2016,35(6):723~730

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Received:May 24,2016
Revised:September 30,2016
Adopted:June 07,2016
Online: December 06,2016
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