Improving Component Substitution Pansharpening Through Multivariate Regression of MS+Pan Data (Articolo in rivista)

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Label
  • Improving Component Substitution Pansharpening Through Multivariate Regression of MS+Pan Data (Articolo in rivista) (literal)
Anno
  • 2007-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1109/TGRS.2007.901007 (literal)
Alternative label
  • Bruno Aiazzi; Stefano Baronti; Massimo Selva (2007)
    Improving Component Substitution Pansharpening Through Multivariate Regression of MS+Pan Data
    in IEEE transactions on geoscience and remote sensing; 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; Massimo Selva (literal)
Pagina inizio
  • 3230 (literal)
Pagina fine
  • 3239 (literal)
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  • http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4305344 (literal)
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  • 45 (literal)
Rivista
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  • 10 (literal)
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  • 10 (literal)
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  • Scopu (literal)
  • Google Scholar (literal)
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Institute of Applied Physics \"Nello Carrara\" CNR Area di Ricerca di Firenze (IFAC-CNR) Institute of Applied Physics \"Nello Carrara\" CNR Area di Ricerca di Firenze (IFAC-CNR) Institute of Applied Physics \"Nello Carrara\" CNR Area di Ricerca di Firenze (IFAC-CNR) (literal)
Titolo
  • Improving Component Substitution Pansharpening Through Multivariate Regression of MS+Pan Data (literal)
Abstract
  • In this paper, multivariate regression is adopted to improve spectral quality, without diminishing spatial quality, in image fusion methods based on the well-established component substitution (CS) approach. A general scheme that is capable of modeling any CS image fusion method is presented and discussed. According to this scheme, a generalized intensity component is defined as the weighted average of the multispectral (MS) bands. The weights are obtained as regression coefficients between the MS bands and the spatially degraded panchromatic (Pan) image, with the aim of capturing the spectral responses of the sensors. Once it has been integrated into the Gram-Schmidt spectralsharpening method, which is implemented in Environment for Visualizing Images (ENVI) program, and into the generalized intensity-hue-saturation fusion method, the proposed preprocessing module allows the production of fused images of the same spatial sharpness but of increased spectral quality with respect to the standard implementations. In addition, quantitative scores carried out on spatially degraded data clearly confirm the superiority of the enhanced methods over their baselines. (literal)
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