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Canadian Journal of Remote Sensing
Journal canadien de télédétection
Volume 48, 2022 - Issue 5: Advances in Hyperspectral Remote Sensing
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Research Note

Hyperspectral Imagery Denoising Using Minimum Noise Fraction and Video Non-Local Bayes Algorithms

Réduction du bruit des images hyperspectrales à l’aide des algorithmes “Minimum Noise Fraction” et “Video Non-Local Bayes”

Guang Yi Chena Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, H3G 1M8, CanadaCorrespondence[email protected]
,
Adam Krzyzaka Department of Computer Science and Software Engineering, Concordia University, Montreal, QC, H3G 1M8, Canada
&
Shen-En Qianb Space Science and Technology, Canadian Space Agency, St-Hubert, QC, J3Y 8Y9, Canada
Pages 694-701 | Received 10 May 2022, Accepted 16 Aug 2022, Published online: 06 Sep 2022

References

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  • Luo, G.C., Chen, G.Y., Tian, L., Qin, K., and Qian, S.-E. 2016. “Minimum Noise Fraction versus Principal Component Analysis as a Preprocessing Step for Hyperspectral Imagery Denoising.” Canadian Journal of Remote Sensing, Vol. 42(No. 2): pp. 106–116. doi:10.1080/07038992.2016.1160772.
  • Mates, D.M., Zwick, H., Jolly, G., and Schulten, D. 2004. System Studies of a Small Satellite Hyperspectral Mission, Data Acceptability, Can. Gov. Contract Rep. HYTN-51-4972. Richmond: Macdonald, Dettwiller, Assoc.

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