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Intelligent Engineering Systems through Artificial Neural Networks, Volume 20

Cihan H. Dagli
Cihan H. Dagli
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ASME Press
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Hyperspectral imagery (HSI) has emerged as a valuable tool supporting numerous military and commercial missions. Environmental and other effects diminish HSI classification accuracy. Thus there is a desire to create robust classifiers that perform well in all possible environments. Robust parameter design (RPD) techniques have been applied to determine optimal operating settings. Previous RPD efforts considered an HSI image as categorical noise. This paper presents a novel method utilizing discrete and continuous image characteristics as representations of the noise present. Specifically, the number of clusters, fisher ratio and percent of target pixels were used to generate image training and test sets. Replacing categorical noise with the new image characteristics improves RPD results by correctly accounting for significant terms in the regression model that were otherwise considered categorical factors. Further, traditional RPD assumptions of independent noise variables are invalid for the selected HSI images.

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