Comparison of nine hyperspectral pansharpening methods

Laetitia Loncan, Luís B Almeida, Jose Bioucas Dias, Xavier Briottet, Jocelyn Chanussot, Nicolas Dobigeon, Sophie Fabre, Wenzhi Liao, Giorgio Licciardi, Miguel Simoes, Jean-Yves Tourneret, Miguel A Veganzones, Gemine Vivone, Qi Wei, Naoto Yokoya, Vito Pascazio

Research output: Contribution to conferencePaperpeer-review


Pansharpening first aims at fusing a panchromatic image with a multispectral image to generate an image with the high spatial resolution of the former and the spectral resol ution of the latter. In the last decade many algorithms have been presented in the literature for pansharpening usi ng multispectral data. With the increasing availability of hyperspectral systems these methods are now extending to hyperspectral images. In this work, we attempt to compare new pansharpening techniques designed for hypersp ectral data with some of the state of the art methods for multispectral pansharpening, which have been adapted f or hyperspectral data. Nine methods from different classes are analysed: component substitution, multiresol ution analysis, hybrid, Bayesian and matrix decomposition approaches. These techniques are evaluated with the Wald’s Procol on one dataset to characterize their performances and their robustness.
Original languageEnglish
Number of pages4
Publication statusPublished - 2015
EventIEEE International Geoscience and Remote Sensing Sympium (IGARSS 2015) - Convention Center, Milan, Italy
Duration: 26 Jul 201531 Jul 2015


ConferenceIEEE International Geoscience and Remote Sensing Sympium (IGARSS 2015)
Abbreviated titleIGARSS 2015


  • hyperspectral pansharpening methods
  • multispectral image
  • multispectral data

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