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Deployment of pansharpening for correction of local misalignments between MS and Pan
Author(s): B. Aiazzi; L. Alparone; A. Arienzo; S. Baronti; A. Garzelli; L. Santurri
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Paper Abstract

In this paper, a simple and totally unsupervised image-based procedure is derived for the alignment of interpolated multispectral (MS) bands over the panchromatic (Pan) image. Key point of the method is the pixel-varying residue of the multivariate regression between interpolated MS bands and lowpass-filtered Pan image that is used for component-substitution (CS) pansharpening to produce a minimum mean squared error (MMSE) intensity component. Such a residue locally measures the misalignment between the datasets and, once it has been properly weighted by the projection coefficient of the MMSE intensity onto the kth band, the overlap of the lowpass and highpass components of the scene is recovered. Experiments performed on simulated Pleiades and true GeoEye-1 images show that local shifts, reasonably around two ´ or three pixels in each direction, can be largely mitigated. The coefficient of determination (CD) of the multivariate regression of interpolated MS images to lowpass filtered Pan is used to globally quantify the alignment of datasets before and after the proposed pre-processing patch. The CD of the multivariate regression of pansharpened MS bands to original high-resolution Pan is a consistent full-scale measure of spatial quality of pansharpened products.

Paper Details

Date Published: 9 October 2018
PDF: 11 pages
Proc. SPIE 10789, Image and Signal Processing for Remote Sensing XXIV, 1078902 (9 October 2018); doi: 10.1117/12.2326611
Show Author Affiliations
B. Aiazzi, Institute of Applied Physics, IFAC-CNR (Italy)
L. Alparone, Univ. of Florence (Italy)
A. Arienzo, Univ. of Florence (Italy)
S. Baronti, Institute of Applied Physics, IFAC-CNR (Italy)
A. Garzelli, Univ. of Siena (Italy)
L. Santurri, Institute of Applied Physics, IFAC-CNR (Italy)


Published in SPIE Proceedings Vol. 10789:
Image and Signal Processing for Remote Sensing XXIV
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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