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Proceedings Paper

Quantitative comparison of multispectral image-sharpening algorithms
Author(s): Tim J. Patterson; Robert Stu Haxton; Michael E. Bullock; Stephen B. Ulinski
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Paper Abstract

This paper presents a quantitative comparison of a multispectral sharpening algorithm, which was introduced previously, with other standard techniques. The sharpening algorithms combine a high spatial resolution panchromatic image with a lower resolution multispectral image. The combination is based on a pseudo inverse of the image formation equations. The paper begins with an introduction motivating the technique. Previous approaches to the sharpening problem are then outlined. This is followed by a description of two new approaches. The first is an improvement on the standard intensity saturation hue (ISH) transform and the second is a sharpener which was introduced in previous papers. After descriptions of the sharpeners, the more important results of a series of experiments to evaluate the sharpener performance are presented. A full series of tests in which low resolution multispectral data was synthesized from a high resolution scene, sharpened with various techniques and compared to the original high resolution imagery was conducted. The most significant results are presented in this paper. A second test was conducted using both high and low resolution images collected of the same area. Sharpened low resolution multispectral images were compared to actual high resolution imagery of the same area.

Paper Details

Date Published: 17 June 1996
PDF: 12 pages
Proc. SPIE 2758, Algorithms for Multispectral and Hyperspectral Imagery II, (17 June 1996); doi: 10.1117/12.243212
Show Author Affiliations
Tim J. Patterson, Booz-Allen & Hamilton Inc. (United States)
Robert Stu Haxton, Booz-Allen & Hamilton Inc. (United States)
Michael E. Bullock, Booz-Allen & Hamilton Inc. (United States)
Stephen B. Ulinski, Booz-Allen & Hamilton Inc. (United States)

Published in SPIE Proceedings Vol. 2758:
Algorithms for Multispectral and Hyperspectral Imagery II
A. Evan Iverson, Editor(s)

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