Paper
23 October 2014 Full scale assessment of pansharpening methods and data products
Author Affiliations +
Proceedings Volume 9244, Image and Signal Processing for Remote Sensing XX; 924402 (2014) https://doi.org/10.1117/12.2067770
Event: SPIE Remote Sensing, 2014, Amsterdam, Netherlands
Abstract
Quality assessment of pansharpened images is traditionally carried out either at degraded spatial scale by checking the synthesis property ofWald’s protocol or at the full spatial scale by separately checking the spectral and spatial consistencies. The spatial distortion of the QNR protocol and the spectral distortion of Khan’s protocol may be combined into a unique quality index, referred to as hybrid QNR (HQNR), that is calculated at full scale. Alternatively, multiscale measurements of indices requiring a reference, like SAM, ERGAS and Q4, may be extrapolated to yield a quality measurement at the full scale of the fusion product, where a reference does not exist. Experiments on simulated Pl´eiades data, of which reference originals at full scale are available, highlight that quadratic polynomials having three-point support, i.e. fitting three measurements at as many progressively doubled scales, are adequate. Q4 is more suitable for extrapolation than ERGAS and SAM. The Q4 value predicted from multiscale measurements and the Q4 value measured at full scale thanks to the reference original, differ by very few percents for six different state-of-the-art methods that have been compared. HQNR is substantially comparable to the extrapolated Q4.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
B. Aiazzi, L. Alparone, S. Baronti, R. Carlà, A. Garzelli, and L. Santurri "Full scale assessment of pansharpening methods and data products", Proc. SPIE 9244, Image and Signal Processing for Remote Sensing XX, 924402 (23 October 2014); https://doi.org/10.1117/12.2067770
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Cited by 18 scholarly publications.
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KEYWORDS
Distortion

Image fusion

Image resolution

Spatial resolution

Image quality

Data fusion

Spectral resolution

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