The majority of pansharpening methods can be classified as spectral or spatial methods, depending on whether they are based on component substitution (CS) or multiresolution analysis (MRA). So far, the suitability of one class or methods rather than another has been seldom discussed. In this paper, through experiments on IKONOS and simulated Pleíades datasets, the authors demonstrate that the performances of spectral methods depend on the extent of spectral matching, measured by the coefficient of determination (CD) of the multivariate regression between MS and P. For data with simulated P, CD is very close to one and all methods perform almost identically. For true IKONOS datasets, the CD is few percent lower and spatial methods, once they have been optimized through the knowledge of the modulation transfer function (MTF) of the imaging system, are always more performing than spectral methods. Since spatial methods are unaffected by the spectral matching, they are preferable whenever such an issue is critical, e.g., for hyperspectral pansharpening.

Are spectral or spatial methods better for pansharpening? An evaluation for four sample methods based on spatial modulation of pixel spectra / Alparone, Luciano; Garzelli, Andrea; Vivone, Gemine. - ELETTRONICO. - 9643:(2015), pp. 1-12. (Intervento presentato al convegno Image and Signal Processing for Remote Sensing XXI tenutosi a Toulouse, France nel 2015) [10.1117/12.2196193].

Are spectral or spatial methods better for pansharpening? An evaluation for four sample methods based on spatial modulation of pixel spectra

ALPARONE, LUCIANO;
2015

Abstract

The majority of pansharpening methods can be classified as spectral or spatial methods, depending on whether they are based on component substitution (CS) or multiresolution analysis (MRA). So far, the suitability of one class or methods rather than another has been seldom discussed. In this paper, through experiments on IKONOS and simulated Pleíades datasets, the authors demonstrate that the performances of spectral methods depend on the extent of spectral matching, measured by the coefficient of determination (CD) of the multivariate regression between MS and P. For data with simulated P, CD is very close to one and all methods perform almost identically. For true IKONOS datasets, the CD is few percent lower and spatial methods, once they have been optimized through the knowledge of the modulation transfer function (MTF) of the imaging system, are always more performing than spectral methods. Since spatial methods are unaffected by the spectral matching, they are preferable whenever such an issue is critical, e.g., for hyperspectral pansharpening.
2015
Proceedings of SPIE - The International Society for Optical Engineering
Image and Signal Processing for Remote Sensing XXI
Toulouse, France
2015
Alparone, Luciano; Garzelli, Andrea; Vivone, Gemine
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1071678
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