Multiple-Spectral-Band CRFs for Denoising Junk Bands of Hyperspectral Imagery

  title={Multiple-Spectral-Band CRFs for Denoising Junk Bands of Hyperspectral Imagery},
  author={Ping Zhong and Runsheng Wang},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
Denoising of hyperspectral imagery in the domain of imaging spectroscopy by conditional random fields (CRFs) is addressed in this work. For denoising of hyperspectral imagery, the strong dependencies across spatial and spectral neighbors have been proved to be very useful. Many available hyperspectral image denoising algorithms adopt multidimensional tools to deal with the problems and thus naturally focus on the use of the spectral dependencies. However, few of them were specifically designed… CONTINUE READING
Highly Cited
This paper has 46 citations. REVIEW CITATIONS


Publications citing this paper.
Showing 1-10 of 31 extracted citations


Publications referenced by this paper.
Showing 1-10 of 56 references

Simultaneous dimensionality reduction and denoising of hyperspectral imagery using bivariate wavelet shrinking and principal component analysis,

  • G. Chen, S. Qian
  • Can. J. Remote Sens., vol. 34,
  • 2008
Highly Influential
20 Excerpts

Similar Papers

Loading similar papers…