http://www.cnr.it/ontology/cnr/individuo/prodotto/ID135053
The curvelet transform for fusion of very-high resolution multispectral and panchromatic images (Contributo in atti di convegno)
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- The curvelet transform for fusion of very-high resolution multispectral and panchromatic images (Contributo in atti di convegno) (literal)
- Anno
- 2006-01-01T00:00:00+01:00 (literal)
- Alternative label
L. Alparone, S. Baronti, A. Garzelli, F. Nencini (2006)
The curvelet transform for fusion of very-high resolution multispectral and panchromatic images
in 25th EARSeL Symposium, Porto, 6-11 June 2005
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- L. Alparone, S. Baronti, A. Garzelli, F. Nencini (literal)
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- Global Developments in Environmental Earth Observation from Space (literal)
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- L. Alparone, S. Baronti, A. Garzelli, F. Nencini (literal)
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- p. 95-102, Millpress Rotterdam Netherlands (literal)
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- Alparone L.: Università di Firenze
Garzelli A., Nencini F.: Università di Siena (literal)
- Titolo
- The curvelet transform for fusion of very-high resolution multispectral and panchromatic images (literal)
- Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#inCollana
- Global Developments in Environmental Earth Observation from Space (literal)
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- Abstract
- This paper presents a novel image fusion method, suitable for pan-sharpening of
multispectral (MS) bands, based on multi-resolution analysis (MRA). The low-resolution MS
bands are sharpened by injecting high-pass directional details extracted from the high-resolution
panchromatic (Pan) image by means of the curvelet transform, which is a non-separable MRA,
whose basis function are directional edges with progressively increasing resolution. The advantage
with respect to conventional separable MRA, either decimated or not, is twofold: directional detail
coefficients matching image edges may be preliminarily soft-thresholded to achieve denoising
better than in the separable wavelet domain; modeling of the relationships between high-resolution
detail coefficients of MS bands and of the Pan image is more fitting, being carried out in a
directional wavelet domain. Experiments carried out on a very-high resolution MS + Pan QuickBird
image show that the proposed curvelet method quantitatively outperforms state-of-the art image
fusion methods, in terms of geometric, radiometric, and spectral fidelity (literal)
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