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Proceedings Paper

A theoretical evaluation of aliasing and misregistration effects on pansharpening methods
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Paper Abstract

The characteristics of multispectral (MS) and panchromatic (P) image fusion, or pansharpening, methods are investigated. Depending on the way spatial details are extracted from P, such methods can be broadly labeled into two main classes, roughly corresponding to component substitution (CS), also known as projection substitution, and methods based on multiresolution analysis (MRA), i.e. on digital filtering. Theoretical and experimental results carried out on QuickBird and Ikonos data sets evidence that CS-based fusion is far less sensitive than MRA-based fusion to registration errors, i.e. spatial misalignments between MS and P images, possibly originated by cartographic projection and resampling of individual data sets, and aliasing occurring in MS bands and deriving from a modulation transfer function (MTF) of each MS channel that is excessively broad relatively to the spatial sampling interval. Simulated misalignments carried out at full scale by means of a suitable quality evaluation protocol have evidenced the quality-shift tradeoff of the two classes: MRA methods yield a slightly superior quality in the absence of misalignments, but are more penalized, whenever shifts between MS and P are present, than CS methods producing a slightly lower quality in the ideal case, but that are intrinsically more shift tolerant.

Paper Details

Date Published: 13 October 2010
PDF: 9 pages
Proc. SPIE 7830, Image and Signal Processing for Remote Sensing XVI, 783002 (13 October 2010); doi: 10.1117/12.865980
Show Author Affiliations
Bruno Aiazzi, Istituto di Fisica Applicata Nello Carrara, CNR (Italy)
Luciano Alparone, Univ. degli Studi di Firenze (Italy)
Stefano Baronti, Istituto di Fisica Applicata Nello Carrara, CNR (Italy)
Andrea Garzelli, Univ. degli Studi di Siena (Italy)
Massimo Selva, Istituto di Fisica Applicata Nello Carrara, CNR (Italy)


Published in SPIE Proceedings Vol. 7830:
Image and Signal Processing for Remote Sensing XVI
Lorenzo Bruzzone, Editor(s)

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