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Proceedings Paper

Evolving military-grade image transforms using state-of-the-art variation operators
Author(s): Michael R. Peterson; Gary B. Lamont; Frank Moore; Patrick Marshall
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Paper Abstract

Military imaging systems often require the transmission of copious amounts of data in noisy or bandwidth-limited situations. High rates of lossy image compression may be achieved through the use of quantization at the expense of resulting image quality. We employ genetic algorithms (GAs) to evolve military-grade transforms capable of improving reconstruction of satellite reconnaissance images under conditions subject to high quantization error. The resulting transforms outperform existing wavelet transforms at a given compression ratio allowing transmission of data at a lower bandwidth. Because GAs are notoriously difficult to tune, the selection of appropriate variation operators is critical when designing GAs for military-grade algorithm development. We test several state-of-the-art real-coded crossover and mutation operators to develop an evolutionary system capable of producing transforms providing robust performance over a set of fifty satellite images of military interest. With appropriate operators, evolved filters consistently provide an average mean squared error (MSE) reduction greater than 17% over the original wavelet transform. By improving image quality, evolved transforms increase the amount of intelligence that may be obtained reconstructed images.

Paper Details

Date Published: 2 May 2007
PDF: 12 pages
Proc. SPIE 6563, Evolutionary and Bio-inspired Computation: Theory and Applications, 65630H (2 May 2007); doi: 10.1117/12.720920
Show Author Affiliations
Michael R. Peterson, Wright State Univ. (United States)
Gary B. Lamont, Air Force Institute of Technology (United States)
Frank Moore, Univ. of Alaska, Anchorage (United States)
Patrick Marshall, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 6563:
Evolutionary and Bio-inspired Computation: Theory and Applications
Misty Blowers; Alex F. Sisti, Editor(s)

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