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Proceedings Paper

Methods and metrics for the assessment of Pan-sharpening algorithms
Author(s): Francesca Despini; Sergio Teggi; Andrea Baraldi
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Paper Abstract

Recent remote sensing applications require sensors that provide both high spatial and spectral resolution, but this is often not possible for economic and constructive reasons. The "fusion" of images at different spatial and spectral resolution is a method widely used to solve this problem. Pan-sharpening techniques have been applied in this work to simulate PRISMA images. The work presented here is indeed part of the Italian Space Agency project “ASI-AGI”, which includes the study of a new platform, PRISMA, consisting of an hyperspectral sensor with a spatial resolution of 30 m and a panchromatic sensor with a spatial resolution of 5 m, for monitoring and understanding the Earth's surface. Firstly, PRISMA images have been simulated using images from MIVIS and Quickbird sensors. Then several existing fusion methods have been tested in order to identify the most suitable for the platform PRISMA in terms of spatial and spectral information preservation. Both standard and wavelet algorithms have been used: among the former there are Principal Component Analysis and Gram-Schmidt transform, and among the latter are Discrete Wavelet Transform and the “à trous” wavelet transform. Also the Color Normalized Spectral Sharpening method has been used. Numerous quality metrics have been used to evaluate spatial and spectral distortions introduced by pan-sharpening algorithms. Various strategies can be adopted to provide a final rank of alternative algorithms assessed by means of a battery of quality indexes. All implemented statistics have been standardized and then three different methodologies have been used to achieve a final score and thus a classification of pan-sharpening algorithms. Currently a new protocol is under development to evaluate the preservation of spatial and spectral information in fusion methods. This new protocol should overcome the limitations of existing alternative approaches and be robust to changes in the input dataset and user-defined parameters.

Paper Details

Date Published: 13 October 2014
PDF: 14 pages
Proc. SPIE 9244, Image and Signal Processing for Remote Sensing XX, 924403 (13 October 2014); doi: 10.1117/12.2067316
Show Author Affiliations
Francesca Despini, Univ. of Modena and Reggio Emilia (Italy)
Sergio Teggi, Univ. of Modena and Reggio Emilia (Italy)
Andrea Baraldi, Univ. of Maryland (United States)

Published in SPIE Proceedings Vol. 9244:
Image and Signal Processing for Remote Sensing XX
Lorenzo Bruzzone, Editor(s)

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