Analysis of terahertz spectral images of explosives and bio-agents using trained neural networks

Document Type

Conference Proceeding

Publication Date

12-20-2004

Abstract

A non-invasive means to detect and characterize concealed agents of mass destruction in near real-time with a wide field-of-view is under development. The method employs spatial interferometric imaging of the characteristic transmission or reflection frequency spectrum in the Terahertz range. However, the successful (i.e. low false alarm rate) analysis of such images will depend on correct distinction of the true agent from non-lethal background signals. Neural networks are being trained to successfully distinguish images of explosives and bioagents from images of harmless items. Artificial neural networks are mathematical devices for modeling complex, non-linear relationships. Both multilayer perception and radial basis function neural network architectures are used to analyze these spectral images. Positive identifications are generally made, though, neural network performance does deteriorate with reduction in frequency information. Internal tolerances within the identification process can affect the outcome.

Identifier

10044277017 (Scopus)

Publication Title

Proceedings of SPIE the International Society for Optical Engineering

External Full Text Location

https://doi.org/10.1117/12.542648

ISSN

0277786X

First Page

45

Last Page

50

Volume

5411

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