TY - GEN
T1 - Nonseparable sparsity based hyperspectral compressive sensing
AU - Zhang, Lei
AU - Wei, Wei
AU - Zhang, Yanning
AU - Li, Fei
AU - Yan, Hangqi
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/7/2
Y1 - 2015/7/2
N2 - Accurate reconstruction of hyperspectral image(HSI) from a few random sampled measurements is crucial for hyperspectal compressive sensing. The underlying sparsity of HSI is one crucial factor for HSI reconstruction. However, the s-parsity is unknown in reality and varied with different noise. To address this problem, a novel nonseparable sparsity based hyperspectral compressive sensing(NSHCS) method is proposed in this study. We use empirical Bayes to deduce a non-separable sparsity constraint. The underlying correlation among sparse coefficients in signal is modeled implicitly by this sparsity constraint. Since parameters of this constraint are determined by the sampled measurements and the noise term together, the learned sparsity constraint can be adaptive to different noise. With this constraint, NSHCS can reconstruct the HSI precisely. Experimental results demonstrate the superiority of the proposed method over several state-of-the-art hyperspectral compressive sensing methods in HSI reconstruction.
AB - Accurate reconstruction of hyperspectral image(HSI) from a few random sampled measurements is crucial for hyperspectal compressive sensing. The underlying sparsity of HSI is one crucial factor for HSI reconstruction. However, the s-parsity is unknown in reality and varied with different noise. To address this problem, a novel nonseparable sparsity based hyperspectral compressive sensing(NSHCS) method is proposed in this study. We use empirical Bayes to deduce a non-separable sparsity constraint. The underlying correlation among sparse coefficients in signal is modeled implicitly by this sparsity constraint. Since parameters of this constraint are determined by the sampled measurements and the noise term together, the learned sparsity constraint can be adaptive to different noise. With this constraint, NSHCS can reconstruct the HSI precisely. Experimental results demonstrate the superiority of the proposed method over several state-of-the-art hyperspectral compressive sensing methods in HSI reconstruction.
KW - hyperspectral compressive sensing
KW - hyperspectral image compression
KW - Nonseparable sparsity
UR - http://www.scopus.com/inward/record.url?scp=85039155185&partnerID=8YFLogxK
U2 - 10.1109/WHISPERS.2015.8075427
DO - 10.1109/WHISPERS.2015.8075427
M3 - 会议稿件
AN - SCOPUS:85039155185
T3 - Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
BT - 2015 7th Workshop on Hyperspectral Image and Signal Processing
PB - IEEE Computer Society
T2 - 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2015
Y2 - 2 June 2015 through 5 June 2015
ER -