TY - GEN
T1 - Structured sparse Bayesian hyperspectral compressive sensing using spectral unmixing
AU - Zhang, Lei
AU - Wei, Wei
AU - Zhang, Yanning
AU - Li, Fei
AU - Yan, Hangqi
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/6/28
Y1 - 2014/6/28
N2 - To reduce huge consumption of processing hyperspectral images(HSI), a novel Bayesian unmixing compressive sensing framework is proposed to compress and reconstruct HSI effectively, called structured sparse Bayesian umixing compressive sensing(SSBUCS). SSBUCS unites compressive sensing and hyperspectral linear mixed model in Bayesian framework. An HSI is decomposed as a linear combination of endmembers and abundance matrix. The abundance matrix is transformed to a structured sparse signal in the wavelet domain. Then, compressive sensing is employed on this sparse signal to produce a more compact result. To recover the HSI, a Markov chain Monte Carlo(MCMC) method based on Gibbs sampling is proposed, imposing structured sparse prior on abundance matrix. Experimental results verify the superiority of the proposed method over several state-of-art methods.
AB - To reduce huge consumption of processing hyperspectral images(HSI), a novel Bayesian unmixing compressive sensing framework is proposed to compress and reconstruct HSI effectively, called structured sparse Bayesian umixing compressive sensing(SSBUCS). SSBUCS unites compressive sensing and hyperspectral linear mixed model in Bayesian framework. An HSI is decomposed as a linear combination of endmembers and abundance matrix. The abundance matrix is transformed to a structured sparse signal in the wavelet domain. Then, compressive sensing is employed on this sparse signal to produce a more compact result. To recover the HSI, a Markov chain Monte Carlo(MCMC) method based on Gibbs sampling is proposed, imposing structured sparse prior on abundance matrix. Experimental results verify the superiority of the proposed method over several state-of-art methods.
KW - Bayesian Compressive Sensing
KW - Gibbs Sampling
KW - Hyperspectral compression
KW - Structured Sparsity
UR - http://www.scopus.com/inward/record.url?scp=85038574984&partnerID=8YFLogxK
U2 - 10.1109/WHISPERS.2014.8077636
DO - 10.1109/WHISPERS.2014.8077636
M3 - 会议稿件
AN - SCOPUS:85038574984
T3 - Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
BT - 2014 6th Workshop on Hyperspectral Image and Signal Processing
PB - IEEE Computer Society
T2 - 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2014
Y2 - 24 June 2014 through 27 June 2014
ER -