13 April 2009 Explosive detection in the presence of clutter by processing Raman spectra with a kernel adatron
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Abstract
Raman spectra have fingerprint regions that are highly distinctive and can in principle be used for identification of explosive residues. However, under most field situations strong illumination by sunlight, impurities in the explosives, or the presence of a substrate or matrix, cause the Raman spectra to have a strong fluorescence background. Using spectra of pure explosives, spectra of highly-fluorescent clutter materials including asphalt, cement, sand, soil, and paint chips, and some spectra of pre-mixed explosive and clutter, we synthesized a library of admixed spectra varying from 5% explosives and 95% clutter spectra up to 100% explosives and 0% clutter spectra. This represented a signal to noise ratio for the explosive peaks varying from 0.04 to 5933. Using this library to train a support vector machine, known as a kernel adatron, we obtained very good identification of the explosive vs. non-explosive. We performed a 40-fold crossvalidation with leave-100-out for evaluation. Our results show 99.8% correct classification with 0.2% false positives.
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Amy E. Stevens, Amy E. Stevens, Patrick T. Rourke, Patrick T. Rourke, Edward A. Rietman, Edward A. Rietman, } "Explosive detection in the presence of clutter by processing Raman spectra with a kernel adatron", Proc. SPIE 7345, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2009, 73450F (13 April 2009); doi: 10.1117/12.817323; https://doi.org/10.1117/12.817323
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