Denoising hyperspectral images using spectral domain statistics

  title={Denoising hyperspectral images using spectral domain statistics},
  author={Antony Lam and Imari Sato and Yoichi Sato},
  journal={Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012)},
Hyperspectral imaging has proven useful in a diverse range of applications in agriculture, diagnostic medicine, and surveillance to name a few. However, conventional hyperspectral images (HSIs) tend to be noisy due to limited light in individual bands; thus making denoising necessary. Previous methods for HSI de-noising have viewed the entire HSI as a general 3D volume or focused on processing the spatial domain. However, past findings suggest that spectral distributions exhibit less variation… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.
10 Citations
14 References
Similar Papers


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


Publications referenced by this paper.
Showing 1-10 of 14 references

Adaptive linear minimum mean square error restoration: influence on hyperspectral detection strategy

  • J.-M. Gaucel, G. Mireille, S. Bourennane
  • Int. J. Remote Sens.,
  • 2008
1 Excerpt

Constrained connectivity and connected filters

  • P. Soille
  • IEEE Trans. PAMI,
  • 2008
1 Excerpt

Similar Papers

Loading similar papers…