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.
Gil Raz, Cara Murphy, Chelsea Georgan, Ross Greenwood, R. K. Prasanth, Travis Myers, Anish Goyal, David Kelley, Derek Wood, and Petros Kotidis, "Novel trace chemical detection algorithms: a comparative study," Proc. SPIE 10198, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIII, 101980D (Presented at SPIE Defense + Security: April 11, 2017; Published: 5 May 2017); https://doi.org/10.1117/12.2258429.
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