›› 2012, Vol. 27 ›› Issue (4): 851-861.doi: 10.1007/s11390-012-1269-1

Special Issue: Artificial Intelligence and Pattern Recognition

• Regular Papers • Previous Articles     Next Articles

Hyperspectral Imagery Denoising Using a Spatial-Spectral Domain Mixing Prior

Shao-Lin Chen (陈绍林), Xi-Yuan Hu (胡晰远), Member, IEEE, and Si-Long Peng (彭思龙)   

  1. National ASIC Design and Engineering Center, Institute of Automation, Chinese Academy of Sciences Beijing 100190, China
  • Received:2011-10-23 Revised:2012-05-11 Online:2012-07-05 Published:2012-07-05
  • Supported by:

    This work is supported in part by the National Natural Science Foundation of China under Grant Nos. 60972126, 60921061 and the State Key Program of National Natural Science of China under Grant No. 61032007.

By introducing a novel spatial-spectral domain mixing prior, this paper establishes a Maximum a posteriori (MAP) framework for hyperspectral images (HSIs) denoising. The proposed mixing prior takes advantage of different properties of HSI in the spatial and spectral domain. Furthermore, we propose a spatially adaptive weighted prior combining smoothing prior and discontinuity-preserving prior in the spectral domain. The weights can be defined as a function of the spectral discontinuity measure (DM). For minimizing the objective function, a half-quadratic optimization algorithm is used. The experimental results illustrate that our proposed model can get a higher signal-to-noise ratio (SNR) than using only smoothing prior or discontinuity-preserving prior.

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