Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


Department of Engineering Physics

First Advisor

Glen P. Perram, PhD


The classification of battlespace detonations, specifically the determination of munitions type and size using temporal and spectral features, has been studied using near-infrared (NIR) and multi-color visible wavelength imagers. Key features from the time dependence of fireball size are identified for discriminating various types and sizes of detonation flashes. The five classes include three weights of trinitrotoluene (TNT) and two weights of an enhanced mixture, all of which are uncased and detonated with 10% C4. Using Fisher linear discriminant techniques, features are projected onto a line such that the projected points are maximally clustered for the different classes of detonations. Bayesian decision boundaries are then established on class-conditional probability densities. Feature saliency and stability are determined by selecting features that best discriminate while requiring low variations in class-conditional probability densities and high performance in independent testing. The most important and stable feature is the time to the maximum fireball area in the near-infrared wavelength band. Overall, the features related to the time to peak (tmp) of the fireball provide the best classification for each of three a priori conditions. This feature correctly discriminates between TNT and ENE about 90% of the time, whether weight is known or not. The associated class-conditional probability densities separate the two classes with a Fisher ratio of 2.9 and an area under the receiver operating characteristic, AROC, of 0.992. Also, tmp achieves approximately 60% success rate at discerning both weight and type.

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