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14 May 2019 Active LWIR hyperspectral imaging and algorithms for rapid standoff trace chemical identification
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Abstract
We are developing a cart-mounted platform for chemical threat detection and identification based on active LWIR imaging spectroscopy. Infrared backscatter imaging spectroscopy (IBIS) leverages IR quantum cascade lasers, tuned through signature absorption bands (6 - 11 μm) in the analytes while illuminating a surface area of interest. An IR focal plane array captures the time-dependent backscattering surface response. The image stream forms a hyperspectral image cube composed of spatial, spectral and temporal dimensions as feature vectors for detection and identification. Our current emphasis is on rapid screening. This manuscript also describes methods for simulating IBIS data and for training detection algorithms based on convolutional neural networks (CNN). We have previously demonstrated standoff trace detection at several meters indoors and in field tests, while operating the lasers below the eye-safe intensity limit (100 mW/cm2). Sensitivity to explosive traces as small as a single grain (~1 ng) has been demonstrated. Analytes tested include RDX, PETN, TNT, ammonium nitrate, caffeine and perchlorates on relevant glass, plastic, metal, and painted substrates.
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Christopher J. Breshike, Christopher A. Kendziora, Norman Budack, Yohan Yoon, Robert Furstenberg, Viet Nguyen, and R. Andrew McGill "Active LWIR hyperspectral imaging and algorithms for rapid standoff trace chemical identification", Proc. SPIE 10986, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imagery XXV, 109860K (14 May 2019); https://doi.org/10.1117/12.2518720
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