Trace chemical detection and classification in stand-off reflection-based spectroscopic data is challenging due to the variability of measured data and the lack of physics-based models that can accurately predict spectra. Most available models assume that the chemical takes the form of spherical particles or uniform thin films. A more realistic chemical presentation that could be encountered is that of a nonuniform chemical film that is deposited after evaporation of the solvent that contained the chemical. We present an improved signature model for this type of solid film. The proposed model, called sparse transfer matrix, includes a log-normal distribution of film thicknesses and is found to reduce the root mean square error between simulated and measured data by about 25% when compared with either the particle or uniform thin film models. When applied to measured data, the sparse transfer matrix model provides a 10% to 28% increase in classification accuracy over traditional models.
KEYWORDS: Sensors, Absorption, Detection and tracking algorithms, Target detection, Signal attenuation, Hyperspectral imaging, Chemical analysis, Reflectivity, Principal component analysis, Chemical detection
Real-time, standoff detection of trace chemicals on surfaces in the presence of unknown interferent contaminants using active IR spectroscopy poses significant challenges. The measurement time and computational burden can be prohibitive due to the number of spatial pixels, hundreds of potential wavenumbers, and size of the chemical library containing thousands of signatures. Therefore it is advantageous to optimally sample a small subset of the possible wavenumbers, where optimality is meant in the sense of detection and classification performance. Our approach accomplishes this by selecting wavenumbers which maximize the information about chemical identity. This is done by using submodular optimization, a technique which guarantees near-optimality at vanishingly low computational burden. Therefore, the methods shown here lend themselves to the time and resource constrained problems of data acquisition. This is in contrast to more traditional dimensionality reduction approaches such as lowering spectral resolution, random sparse sampling, and principal component analysis which degrade detection performance. In this work we describe methods for optimal illumination wavenumber selection to address the time constraints while addressing the challenges imposed by hardware and environmental artifacts (e.g., atmospheric effects).
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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