Hyperspectral pansharpening: a review

Laetitia Loncan, Luís B Almeida, Jose Bioucas- dias, Xavier Briottet, Jocelyn Chanussot, Nicolas Dobigeon, Sophie Fabre, Wenzhi Liao, Giorgio Licciardi, Miguel Simoes, Jean- Yves Tournere, Miguel Veganzones, Gemine Vivone, Qi Wei, Naoto Yokoya, Lorenzo Bruzzone (Editor)

Research output: Contribution to journalArticle

215 Citations (Scopus)

Abstract

Pansharpening aims at fusing a panchromatic image with a multispectral one, to generate an image with the high spatial resolution of the former and the high spectral resolution of the latter. In the last decade, many algorithms have been presented in the literatures for pansharpening using multispectral data. With the increasing availability of hyperspectral systems, these methods are now being adapted to hyperspectral images. In this work, we compare new pansharpening techniques designed for hyperspectral data with some of the state-of-the-art methods for multispectral pansharpening, which have been adapted for hyperspectral data. Eleven methods from different classes (component substitution, multiresolution analysis, hybrid, Bayesian and matrix factorization) are analyzed. These methods are applied to three datasets and their effectiveness and robustness are evaluated with widely used performance indicators. In addition, all the pansharpening techniques considered in this paper have been implemented in a MATLAB toolbox that is made available to the community.
LanguageEnglish
Pages27-46
Number of pages20
JournalIEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
Volume3
Issue number3
DOIs
Publication statusPublished - 30 Sep 2015

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high resolution
panchromatic image
factorization
spectral resolution
availability
spatial resolution
substitutes
substitution
matrices
matrix
method
state of the art
indicator
analysis

Keywords

  • nonnegative matrix factorization
  • data-fusion
  • multispectral image
  • component analysis
  • multiband analysis
  • bayesian analysis
  • map estimation
  • resolution
  • sparse
  • algorithm
  • geophysical image processing
  • mathematics computing

Cite this

Loncan, L., Almeida, L. B., Bioucas- dias, J., Briottet, X., Chanussot, J., Dobigeon, N., ... Bruzzone, L. (Ed.) (2015). Hyperspectral pansharpening: a review. 3(3), 27-46. https://doi.org/10.1109/MGRS.2015.2440094
Loncan, Laetitia ; Almeida, Luís B ; Bioucas- dias, Jose ; Briottet, Xavier ; Chanussot, Jocelyn ; Dobigeon, Nicolas ; Fabre, Sophie ; Liao, Wenzhi ; Licciardi, Giorgio ; Simoes, Miguel ; Tournere, Jean- Yves ; Veganzones, Miguel ; Vivone, Gemine ; Wei, Qi ; Yokoya, Naoto ; Bruzzone, Lorenzo (Editor). / Hyperspectral pansharpening : a review. 2015 ; Vol. 3, No. 3. pp. 27-46.
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Loncan, L, Almeida, LB, Bioucas- dias, J, Briottet, X, Chanussot, J, Dobigeon, N, Fabre, S, Liao, W, Licciardi, G, Simoes, M, Tournere, JY, Veganzones, M, Vivone, G, Wei, Q, Yokoya, N & Bruzzone, L (ed.) 2015, 'Hyperspectral pansharpening: a review' vol. 3, no. 3, pp. 27-46. https://doi.org/10.1109/MGRS.2015.2440094

Hyperspectral pansharpening : a review. / Loncan, Laetitia; Almeida, Luís B; Bioucas- dias, Jose; Briottet, Xavier; Chanussot, Jocelyn; Dobigeon, Nicolas; Fabre, Sophie; Liao, Wenzhi; Licciardi, Giorgio; Simoes, Miguel; Tournere, Jean- Yves; Veganzones, Miguel; Vivone, Gemine; Wei, Qi; Yokoya, Naoto; Bruzzone, Lorenzo (Editor).

Vol. 3, No. 3, 30.09.2015, p. 27-46.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Hyperspectral pansharpening

T2 - a review

AU - Loncan, Laetitia

AU - Almeida, Luís B

AU - Bioucas- dias, Jose

AU - Briottet, Xavier

AU - Chanussot, Jocelyn

AU - Dobigeon, Nicolas

AU - Fabre, Sophie

AU - Liao, Wenzhi

AU - Licciardi, Giorgio

AU - Simoes, Miguel

AU - Tournere, Jean- Yves

AU - Veganzones, Miguel

AU - Vivone, Gemine

AU - Wei, Qi

AU - Yokoya, Naoto

A2 - Bruzzone, Lorenzo

PY - 2015/9/30

Y1 - 2015/9/30

N2 - Pansharpening aims at fusing a panchromatic image with a multispectral one, to generate an image with the high spatial resolution of the former and the high spectral resolution of the latter. In the last decade, many algorithms have been presented in the literatures for pansharpening using multispectral data. With the increasing availability of hyperspectral systems, these methods are now being adapted to hyperspectral images. In this work, we compare new pansharpening techniques designed for hyperspectral data with some of the state-of-the-art methods for multispectral pansharpening, which have been adapted for hyperspectral data. Eleven methods from different classes (component substitution, multiresolution analysis, hybrid, Bayesian and matrix factorization) are analyzed. These methods are applied to three datasets and their effectiveness and robustness are evaluated with widely used performance indicators. In addition, all the pansharpening techniques considered in this paper have been implemented in a MATLAB toolbox that is made available to the community.

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KW - nonnegative matrix factorization

KW - data-fusion

KW - multispectral image

KW - component analysis

KW - multiband analysis

KW - bayesian analysis

KW - map estimation

KW - resolution

KW - sparse

KW - algorithm

KW - geophysical image processing

KW - mathematics computing

UR - http://hdl.handle.net/1854/LU-7011509

U2 - 10.1109/MGRS.2015.2440094

DO - 10.1109/MGRS.2015.2440094

M3 - Article

VL - 3

SP - 27

EP - 46

IS - 3

ER -

Loncan L, Almeida LB, Bioucas- dias J, Briottet X, Chanussot J, Dobigeon N et al. Hyperspectral pansharpening: a review. 2015 Sep 30;3(3):27-46. https://doi.org/10.1109/MGRS.2015.2440094