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 Quickbird) satellite platforms.
Global and local Gram-Schmidt methods for hyperspectral pansharpening
VIVONE, GEMINE;RESTAINO, Rocco;ADDESSO, PAOLO;
2015-01-01
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 Quickbird) satellite platforms.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
