A Comparison Between Global and Context-Adaptive Pansharpening of Multispectral Images (Articolo in rivista)

Type
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  • A Comparison Between Global and Context-Adaptive Pansharpening of Multispectral Images (Articolo in rivista) (literal)
Anno
  • 2009-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1109/LGRS.2008.2012003 (literal)
Alternative label
  • Bruno Aiazzi; Stefano Baronti; Franco Lotti; Massimo Selva (2009)
    A Comparison Between Global and Context-Adaptive Pansharpening of Multispectral Images
    in IEEE geoscience and remote sensing letters (Print); IEEE-Institute Of Electrical And Electronics Engineers Inc., Piscataway (Stati Uniti d'America)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Bruno Aiazzi; Stefano Baronti; Franco Lotti; Massimo Selva (literal)
Pagina inizio
  • 302 (literal)
Pagina fine
  • 306 (literal)
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  • http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4776454 (literal)
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  • 6 (literal)
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  • 5 (literal)
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  • 2 (literal)
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  • Scopu (literal)
  • ISI Web of Science (WOS) (literal)
  • Google Scholar (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Istituto di Fisica Applicata \"Nello Carrara,\" Consiglio Nazionale delle Ricerche Area della Ricerca di Firenze Istituto di Fisica Applicata \"Nello Carrara,\" Consiglio Nazionale delle Ricerche Area della Ricerca di Firenze Istituto di Fisica Applicata \"Nello Carrara,\" Consiglio Nazionale delle Ricerche Area della Ricerca di Firenze Istituto di Fisica Applicata \"Nello Carrara,\" Consiglio Nazionale delle Ricerche Area della Ricerca di Firenze (literal)
Titolo
  • A Comparison Between Global and Context-Adaptive Pansharpening of Multispectral Images (literal)
Abstract
  • Multiresolution analysis (MRA) and component substitution (CS) are the two basic frameworks to which image fusion algorithms can be reported when merging multispectral (MS) and panchromatic (Pan) images (pansharpening), acquired with different spatial and spectral resolutions. State-of-the-art algorithms add the spatial details extracted from the Pan into the MS data set by considering different injection strategies. The capability of efficiently modeling the relationships between MS and Pan is crucial for the quality of fusion results and particularly for a correct recovery of local features with a consequent reduction of spectral distortions. Although context-adaptive (CA) injection models have been proposed in the MRA framework, their adoption in CS schemes has been scarcely investigated so far. In this letter, CA strategies are compared with global models by considering a general protocol in which both MRA- and CS-based schemes can be described. Qualitative and quantitative results are reported for three high-resolution data sets from two different sensors, namely, IKONOS and simulated Pléiades. The score gains of well-known and novel quality figures show that CA models are more efficient than global ones. (literal)
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