MS+Pan image fusion by an enhanced Gram-Schmidt spectral sharpening (Contributo in atti di convegno)

Type
Label
  • MS+Pan image fusion by an enhanced Gram-Schmidt spectral sharpening (Contributo in atti di convegno) (literal)
Anno
  • 2007-01-01T00:00:00+01:00 (literal)
Alternative label
  • B. Aiazzi, L. Alparone, S. Baronti, M. Selva (2007)
    MS+Pan image fusion by an enhanced Gram-Schmidt spectral sharpening
    in EARSeL 2006, 26th EARSeL Symposium, Varsavia, Polonia, 29 Maggio-2 Giugno 2006
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • B. Aiazzi, L. Alparone, S. Baronti, M. Selva (literal)
Pagina inizio
  • 113 (literal)
Pagina fine
  • 120 (literal)
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  • Volume pubblicato nel 2007. (literal)
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  • Rotterdam (Netherlands) (literal)
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  • New Developments and Challenges in Remote Sensing Proceedings of the 26th Annual Symposium of the European Association of Remote Sensing Laboratories (EARSeL) (literal)
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  • Proc. EARSeL 2006, 26th EARSeL Symposium, Warsaw, Poland, 29 May-2 Jun. 2006, edited by Millpress, Rotterdam (Netherlands), pp. 113-120 (literal)
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  • 8 (literal)
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  • viene studiata un'implementazione innovativa del noto algoritmo di fusione immagini multispettrali Gram-Schmidt spectral sharpening, per mezzo di un procedimento di ottimazione basato su un algoritmo di regressione lineare. (literal)
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  • IFAC-CNR (literal)
Titolo
  • MS+Pan image fusion by an enhanced Gram-Schmidt spectral sharpening (literal)
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  • New Developments and Challenges in Remote Sensing (literal)
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  • 978-90-5966-053-3 (literal)
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  • Z. Bochenek (literal)
Abstract
  • In this work, a simple pre-processing patch is introduced before the Gram-Schmidt (GS) spectral sharpening method (as implemented in ENVI) such that the resulting fused multi-spectral (MS) data exhibit higher sharpness and spectral quality. This is achieved by defining a generalized intensity (GI) component as a weighted average of the MS bands, with weights taken either as percentages of overlap between the spectral responses of individual bands and the spectral response of panchromatic (Pan), or better as regression coefficients between the MS bands and the decimated Pan image. In the former case, the weights· are pre-calculated for each sensor. In the latter case, the weights are calculated by applying a multivariate regression to the data that are being fused. The above GI component is used as low-resolution approximation of the Pan image. Experimental results carried out on very-high · resolution IKONOS data demonstrate that the proposed enhanced GS adaptive (GSA) method visually outperforms both modes of the ENVI implementation of GS, especially in true colour displays. Quantitative scores performed on spatially degraded data by means of such parameters as Wald's ERGAS and the novel Q4 score index based on quaternion theory, confirm the superiority of the enhanced GS method over its baseline. (literal)
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