Explosive detection in the presence of clutter by processing Raman spectra with a kernel adatron

Amy E. Stevens; Patrick T. Rourke; Edward A. Rietman

doi:10.1117/12.817323

13 April 2009 Explosive detection in the presence of clutter by processing Raman spectra with a kernel adatron

Amy E. Stevens, Patrick T. Rourke, Edward A. Rietman

Proceedings Volume 7345, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2009; 73450F (2009) https://doi.org/10.1117/12.817323
Event: SPIE Defense, Security, and Sensing, 2009, Orlando, Florida, United States

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.

Citation Download Citation

Amy E. Stevens, Patrick T. Rourke, and 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); https://doi.org/10.1117/12.817323

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available