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.
AB - 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.
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
SN - 2168-6831
VL - 3
SP - 27
EP - 46
JO - IEEE Geoscience and Remote Sensing Magazine
JF - IEEE Geoscience and Remote Sensing Magazine
IS - 3
ER -