Laser-based, mid-infrared (MIR) hyperspectral imaging (HSI) has the potential to detect a wide range of trace chemicals on a variety of surfaces under standoff conditions. The major challenge of MIR reflection spectroscopy is that the reflection signatures for surface chemicals can be complex and exhibit significant spectral variability. This paper describes a MIR Hyperspectral Simulator that is being developed to model the reflectance signatures from surfaces including the effects of speckle and other sources of spectral variability. Simulated hypercubes will be compared with experiments.
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.
We report on a standoff chemical detection system using widely tunable external-cavity quantum cascade lasers (ECQCLs) to illuminate target surfaces in the mid infrared (λ = 7.4 – 10.5 μm). Hyperspectral images (hypercubes) are acquired by synchronously operating the EC-QCLs with a LN2-cooled HgCdTe camera. The use of rapidly tunable lasers and a high-frame-rate camera enables the capture of hypercubes with 128 x 128 pixels and >100 wavelengths in <0.1 s. Furthermore, raster scanning of the laser illumination allowed imaging of a 100-cm2 area at 5-m standoff. Raw hypercubes are post-processed to generate a hypercube that represents the surface reflectance relative to that of a diffuse reflectance standard. Results will be shown for liquids (e.g., silicone oil) and solid particles (e.g., caffeine, acetaminophen) on a variety of surfaces (e.g., aluminum, plastic, glass). Signature spectra are obtained for particulate loadings of RDX on glass of <1 μg/cm2.
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