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Proceedings Paper

A novel method to evaluate the performance of pan-sharpening algorithms
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Paper Abstract

Pan-sharpened images are useful in a wide variety of applications. Hence, giving quantitative importance to image quality, depending on the nature of target application, may be required to yield maximum benefit. Current techniques for joint evaluation of spatial and spectral quality without reference do not allow to quantitatively associate importance to the image quality. This work proposes a novel global index based on harmonic mean theory to jointly evaluate the performance of pan-sharpening algorithms without using a reference image. The harmonic mean of relative spatial information gain and relative spectral information preservation provides a unique global index to compare the performance of different algorithms. The proposed approach also facilitates in assigning relevance to either the spectral or spatial quality of an image. The information divergence between the MS bands at lower resolutions and the pansharpened image provides a measure of the spectral fidelity and mean-shift. Mutual information between the original pan and synthetic pan images generated from the MS and pan-sharpen images is used to calculate the relative gain. The relative gain helps to quantify the amount of spatial information injected by the algorithm. A trend comparison of the proposed approach with other quality indexes using well-known pan-sharpening algorithms on high resolution (IKONOS and Quickbird) and medium resolution (LandSat7 ETM+) datasets reveals that the new index can be used to evaluate the quality of pan-sharpened image at the resolution of the pan image without the availability of a reference image.

Paper Details

Date Published: 9 April 2007
PDF: 10 pages
Proc. SPIE 6571, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2007, 657102 (9 April 2007); doi: 10.1117/12.719883
Show Author Affiliations
Vijay P Shah, Mississippi State Univ. (United States)
GeoResources Institute (United States)
Nicolas H. Younan, Mississippi State Univ. (United States)
GeoResources Institute (United States)
Roger L. King, Mississippi State Univ. (United States)
GeoResources Institute (United States)


Published in SPIE Proceedings Vol. 6571:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2007
Belur V. Dasarathy, Editor(s)

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