BBD: A new Bayesian bi-clustering denoising algorithm for IASI-NG hyperspectral images

M. Colom; G. Blanchet; A. Klonecki; O. Lezeaux; E. Pequignot; F. Poustomis; C. Thiebaut; S. Ythier; J. M. Morel

doi:10.1109/WHISPERS.2016.8071745

BBD: A new Bayesian bi-clustering denoising algorithm for IASI-NG hyperspectral images

M. Colom
, G. Blanchet
, A. Klonecki
, O. Lezeaux
, E. Pequignot
, F. Poustomis
, C. Thiebaut
, S. Ythier
, J. M. Morel

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

1 Citation (Scopus)

Abstract

We propose a new denoising method for 3D hyperspectral images for the future MetOp-Second Generation series satellite incorporating the new IASI-NG interferometer, to be launched in 2021. This adaptive method retrieves the data model directly from the input noisy granule, using the following techniques: dual clustering (spectral and spatial), dimensionality reduction by adaptive PCA, and Bayesian denoising. The use of dimensionality reduction by PCA has been already proven an effective denoising technique because of intrinsic data redundancy. We demonstrate here that by combining a local PCA dimensionality reduction with a dual clustering and a Bayesian denoising, it is possible to improve significantly the PSNR with respect to PCA reduction alone. This noise reduction hints at the possibility to multiply of the resolution of the satellite by factor 4, while keeping an acceptable SNR. © 2016 IEEE.

Original language	English
Title of host publication	2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2016
Publisher	IEEE Computer Society
Volume	0
ISBN (Print)	9781509006083
DOIs	https://doi.org/10.1109/WHISPERS.2016.8071745
Publication status	Published - 28 Jun 2016
Externally published	Yes
Event	8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2016 - Los Angeles, United States Duration: 21 Aug 2016 → 24 Aug 2016

Publication series

Name	Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing
Volume	0
ISSN (Print)	2158-6276

Conference

Conference	8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2016
Place	United States
City	Los Angeles
Period	21/08/16 → 24/08/16

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Funding

Centred National d'Etudes Snatiales (CNES, R&T action), EUMETSAT, European Research Council (Advanced Grant Twelve Labours), Office of Naval Research (Grant N00014-97-1-0839), Direction Générale de l'Armement (DGA).

Research Keywords

Bayesian
Clustering
Denoising
IASI-NG
PCA

Access Output

10.1109/WHISPERS.2016.8071745

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Colom, M., Blanchet, G., Klonecki, A., Lezeaux, O., Pequignot, E., Poustomis, F., Thiebaut, C., Ythier, S., & Morel, J. M. (2016). BBD: A new Bayesian bi-clustering denoising algorithm for IASI-NG hyperspectral images. In 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2016 (Vol. 0). Article 8071745 (Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing; Vol. 0). IEEE Computer Society. https://doi.org/10.1109/WHISPERS.2016.8071745

@inproceedings{fb3158059292498386481664318dcd00,

title = "BBD: A new Bayesian bi-clustering denoising algorithm for IASI-NG hyperspectral images",

abstract = "We propose a new denoising method for 3D hyperspectral images for the future MetOp-Second Generation series satellite incorporating the new IASI-NG interferometer, to be launched in 2021. This adaptive method retrieves the data model directly from the input noisy granule, using the following techniques: dual clustering (spectral and spatial), dimensionality reduction by adaptive PCA, and Bayesian denoising. The use of dimensionality reduction by PCA has been already proven an effective denoising technique because of intrinsic data redundancy. We demonstrate here that by combining a local PCA dimensionality reduction with a dual clustering and a Bayesian denoising, it is possible to improve significantly the PSNR with respect to PCA reduction alone. This noise reduction hints at the possibility to multiply of the resolution of the satellite by factor 4, while keeping an acceptable SNR. {\textcopyright} 2016 IEEE.",

keywords = "Bayesian, Clustering, Denoising, IASI-NG, PCA",

author = "M. Colom and G. Blanchet and A. Klonecki and O. Lezeaux and E. Pequignot and F. Poustomis and C. Thiebaut and S. Ythier and Morel, \{J. M.\}",

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Colom, M, Blanchet, G, Klonecki, A, Lezeaux, O, Pequignot, E, Poustomis, F, Thiebaut, C, Ythier, S & Morel, JM 2016, BBD: A new Bayesian bi-clustering denoising algorithm for IASI-NG hyperspectral images. in 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2016. vol. 0, 8071745, Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing, vol. 0, IEEE Computer Society, 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2016, Los Angeles, United States, 21/08/16. https://doi.org/10.1109/WHISPERS.2016.8071745

BBD: A new Bayesian bi-clustering denoising algorithm for IASI-NG hyperspectral images. / Colom, M.; Blanchet, G.; Klonecki, A. et al.
2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2016. Vol. 0 IEEE Computer Society, 2016. 8071745 (Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing; Vol. 0).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - BBD

T2 - 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2016

AU - Colom, M.

AU - Blanchet, G.

AU - Klonecki, A.

AU - Lezeaux, O.

AU - Pequignot, E.

AU - Poustomis, F.

AU - Thiebaut, C.

AU - Ythier, S.

AU - Morel, J. M.

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2016/6/28

Y1 - 2016/6/28

N2 - We propose a new denoising method for 3D hyperspectral images for the future MetOp-Second Generation series satellite incorporating the new IASI-NG interferometer, to be launched in 2021. This adaptive method retrieves the data model directly from the input noisy granule, using the following techniques: dual clustering (spectral and spatial), dimensionality reduction by adaptive PCA, and Bayesian denoising. The use of dimensionality reduction by PCA has been already proven an effective denoising technique because of intrinsic data redundancy. We demonstrate here that by combining a local PCA dimensionality reduction with a dual clustering and a Bayesian denoising, it is possible to improve significantly the PSNR with respect to PCA reduction alone. This noise reduction hints at the possibility to multiply of the resolution of the satellite by factor 4, while keeping an acceptable SNR. © 2016 IEEE.

AB - We propose a new denoising method for 3D hyperspectral images for the future MetOp-Second Generation series satellite incorporating the new IASI-NG interferometer, to be launched in 2021. This adaptive method retrieves the data model directly from the input noisy granule, using the following techniques: dual clustering (spectral and spatial), dimensionality reduction by adaptive PCA, and Bayesian denoising. The use of dimensionality reduction by PCA has been already proven an effective denoising technique because of intrinsic data redundancy. We demonstrate here that by combining a local PCA dimensionality reduction with a dual clustering and a Bayesian denoising, it is possible to improve significantly the PSNR with respect to PCA reduction alone. This noise reduction hints at the possibility to multiply of the resolution of the satellite by factor 4, while keeping an acceptable SNR. © 2016 IEEE.

KW - Bayesian

KW - Clustering

KW - Denoising

KW - IASI-NG

KW - PCA

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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85037540472&origin=recordpage

U2 - 10.1109/WHISPERS.2016.8071745

DO - 10.1109/WHISPERS.2016.8071745

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9781509006083

VL - 0

T3 - Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing

BT - 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2016

PB - IEEE Computer Society

Y2 - 21 August 2016 through 24 August 2016

ER -

Colom M, Blanchet G, Klonecki A, Lezeaux O, Pequignot E, Poustomis F et al. BBD: A new Bayesian bi-clustering denoising algorithm for IASI-NG hyperspectral images. In 2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS 2016. Vol. 0. IEEE Computer Society. 2016. 8071745. (Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing). doi: 10.1109/WHISPERS.2016.8071745

BBD: A new Bayesian bi-clustering denoising algorithm for IASI-NG hyperspectral images

Abstract

Publication series

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Research Keywords

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