Global and local Gram-Schmidt methods for hyperspectral pansharpening

Abstract : Pansharpening algorithms enable to produce synthetic data with high spatial details and spectral diversity by combining a panchromatic image with multispectral or hyperspectral data. In classical approaches the details extracted from the panchromatic image are introduced into the original multichannel image through injection gains, which can be spatially variant on the image. In this paper we analyze several methods for partitioning an image into regions in which the pixels will share the same injection coefficients. Gram-Schmidt pansharpening methods are used as paradigmatic examples for assessing the performance of global and local gain estimation strategies, using hyperspectral data acquired by sensors mounted on one (Earth Observing-1) or multiple (PROBA and Quick-bird) satellite platforms.
Type de document :
Communication dans un congrès
IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2015), Jul 2015, Milan, Italy. pp.37 - 40, 〈10.1109/IGARSS.2015.7325691〉
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http://hal.univ-grenoble-alpes.fr/hal-01246566
Contributeur : Vincent Couturier-Doux <>
Soumis le : vendredi 18 décembre 2015 - 17:01:02
Dernière modification le : lundi 9 avril 2018 - 12:22:35

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Mauro Dalla Mura, G Vivone, Rocco Restaino, P. Addesso, Jocelyn Chanussot. Global and local Gram-Schmidt methods for hyperspectral pansharpening. IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2015), Jul 2015, Milan, Italy. pp.37 - 40, 〈10.1109/IGARSS.2015.7325691〉. 〈hal-01246566〉

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