Classical pansharpening algorithms constitute a class of image fusion methods that have been widely investigated in the literature. They have been developed for combining a single- and a multichannel image (panchromatic (PAN) and multispectral (MS), respectively), but can be adapted to the sharpening of hyperspectral (HS) data, both through companion PAN and MS images. We focus in this letter on the HS/MS fusion, showing that the assignation of the MS channel to each HS band is a key step, and investigate several alternatives to make this choice. The assignment algorithms are tested in conjunction with both component substitution and multiresolution analysis pansharpening methods and assessed on images acquired by the Hyperion and ALI sensors. The numerical evaluation shows that the best results can be obtained by optimizing the spectral angle mapper metric confirming that classical methods represent a reliable basis for the development of novel sharpening algorithms.

Band Assignment Approaches for Hyperspectral Sharpening

PICONE, DANIELE;RESTAINO, Rocco;VIVONE, GEMINE;ADDESSO, PAOLO;DALLA MURA, MAURO;
2017

Abstract

Classical pansharpening algorithms constitute a class of image fusion methods that have been widely investigated in the literature. They have been developed for combining a single- and a multichannel image (panchromatic (PAN) and multispectral (MS), respectively), but can be adapted to the sharpening of hyperspectral (HS) data, both through companion PAN and MS images. We focus in this letter on the HS/MS fusion, showing that the assignation of the MS channel to each HS band is a key step, and investigate several alternatives to make this choice. The assignment algorithms are tested in conjunction with both component substitution and multiresolution analysis pansharpening methods and assessed on images acquired by the Hyperion and ALI sensors. The numerical evaluation shows that the best results can be obtained by optimizing the spectral angle mapper metric confirming that classical methods represent a reliable basis for the development of novel sharpening algorithms.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/4685235
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