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Proceedings Paper

Spectral information extraction from very high resolution images through multiresolution fusion
Author(s): Luciano Alparone; Bruno Aiazzi; Stefano Baronti; Andrea Garzelli; Filippo Nencini; Massimo Selva
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Paper Abstract

This paper critically reviews state-of-the-art and advanced methods for multispectral (MS) and panchromatic (Pan) image fusion based on either intensity-hue-saturation (IHS) transformation, or redundant multiresolution analysis (MRA). In either cases, lower-resolution MS bands are sharpened by injecting details taken from the higher-resolution Pan image. Crucial point is modeling the relationships between detail coefficients of a generic MS band and the Pan image at the same resolution. Once calculated at the coarser resolution, where both types of data are available, such a model shall be extended to the finer resolution to weight the Pan details to be injected. Two injection models embedded in an "a trous" wavelet decomposition will be compared on a test set of very high resolution QuickBird MS+Pan data. One works on approximations and provides a partial unmixing of coarse MS pixels via high-resolution Pan. Another is based on spectral fidelity of original and merged MS data. Fusion comparisons on spatially degraded data, whose high-resolution MS originals are available for reference, show that the former performs better than the latter, in terms of both spatial and spectral fidelity.

Paper Details

Date Published: 10 November 2004
PDF: 8 pages
Proc. SPIE 5573, Image and Signal Processing for Remote Sensing X, (10 November 2004); doi: 10.1117/12.606098
Show Author Affiliations
Luciano Alparone, Univ. degli Studi di Firenze (Italy)
Bruno Aiazzi, Nello Carrara Institute of Applied Physics, CNR (Italy)
Stefano Baronti, Nello Carrara Institute of Applied Physics, CNR (Italy)
Andrea Garzelli, Univ. degli Studi di Siena (Italy)
Filippo Nencini, Univ. degli Studi di Siena (Italy)
Massimo Selva, Nello Carrara Institute of Applied Physics, CNR (Italy)

Published in SPIE Proceedings Vol. 5573:
Image and Signal Processing for Remote Sensing X
Lorenzo Bruzzone, Editor(s)

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