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Proceedings Paper

Novel trace chemical detection algorithms: a comparative study
Author(s): Gil Raz; Cara Murphy; Chelsea Georgan; Ross Greenwood; R. K. Prasanth; Travis Myers; Anish Goyal; David Kelley; Derek Wood; Petros Kotidis
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Paper Abstract

Algorithms for standoff detection and estimation of trace chemicals in hyperspectral images in the IR band are a key component for a variety of applications relevant to law-enforcement and the intelligence communities. Performance of these methods is impacted by the spectral signature variability due to presence of contaminants, surface roughness, nonlinear dependence on abundances as well as operational limitations on the compute platforms. In this work we provide a comparative performance and complexity analysis of several classes of algorithms as a function of noise levels, error distribution, scene complexity, and spatial degrees of freedom. The algorithm classes we analyze and test include adaptive cosine estimator (ACE and modifications to it), compressive/sparse methods, Bayesian estimation, and machine learning. We explicitly call out the conditions under which each algorithm class is optimal or near optimal as well as their built-in limitations and failure modes.

Paper Details

Date Published: 5 May 2017
PDF: 18 pages
Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 101980D (5 May 2017); doi: 10.1117/12.2258429
Show Author Affiliations
Gil Raz, Systems & Technology Research (United States)
Cara Murphy, Systems & Technology Research (United States)
Chelsea Georgan, Systems & Technology Research (United States)
Ross Greenwood, Systems & Technology Research (United States)
R. K. Prasanth, Systems & Technology Research (United States)
Travis Myers, Block Engineering, Inc. (United States)
Anish Goyal, Block Engineering, Inc. (United States)
David Kelley, Block Engineering, Inc. (United States)
Derek Wood, Block Engineering, Inc. (United States)
Petros Kotidis, Block Engineering, Inc. (United States)


Published in SPIE Proceedings Vol. 10198:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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