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Proceedings Paper

Fast weighted least squares pan-sharpening
Author(s): Andrea Garzelli; Luciano Alparone; Luca Capobianco; Filippo Nencini
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Paper Abstract

We present a fast pan-sharpening method, namely FWLS, which is based on unsupervised segmentation of the original multispectral (MS) data for improved parameter estimation in a weighted least square fusion scheme. The use of simple thresholding of the normalized difference vegetation index (NDVI) dramatically reduces the computation time with respect to the recently proposed WLS method which is based on accurate supervised classification through kernel support vector machines. The fusion performances of the FWLS algorithm are the same that those obtained by the WLS algorithm, and even higher in some cases, since accurate extraction of vegetated/non-vegetated areas is only needed and high-performance supervised classification is generally not required for fusion parameter estimation. Experimental results and comparisons to state-of-the-art fusion methods are reported on Ikonos and QuickBird data. Both visual and objective quality assessment of the fusion results confirm the validity of the proposed FWLS algorithm.

Paper Details

Date Published: 28 September 2009
PDF: 8 pages
Proc. SPIE 7477, Image and Signal Processing for Remote Sensing XV, 747706 (28 September 2009); doi: 10.1117/12.830417
Show Author Affiliations
Andrea Garzelli, Univ. degli Studi di Siena (Italy)
Luciano Alparone, Univ. degli Studi di Firenze (Italy)
Luca Capobianco, Univ. degli Studi di Siena (Italy)
Filippo Nencini, Univ. degli Studi di Siena (Italy)


Published in SPIE Proceedings Vol. 7477:
Image and Signal Processing for Remote Sensing XV
Lorenzo Bruzzone; Claudia Notarnicola; Francesco Posa, Editor(s)

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