Machine learning algorithms for standoff detection of threat chemicals by active infrared backscatter hyperspectral imaging

Christopher A. Kendziora; Eric Yao; Drew Finton; Drew C. Kendziora; Christopher J. Breshike; Robert Furstenberg

doi:10.1117/12.2663065

13 June 2023 Machine learning algorithms for standoff detection of threat chemicals by active infrared backscatter hyperspectral imaging

Christopher A. Kendziora, Eric Yao, Drew Finton, Drew C. Kendziora, Christopher J. Breshike, Robert Furstenberg

Author Affiliations +

Proceedings Volume 12519, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXIX ; 125190C (2023) https://doi.org/10.1117/12.2663065
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

We are developing machine learning algorithms to identify chemicals of interest by their diffuse infrared (IR) reflectance signatures. For capturing the signatures themselves, we are developing a cart-based mobile system for the detection of trace explosives on surfaces by active infrared (IR) backscatter hyperspectral imaging (HSI). We refer to this technology as Infrared Backscatter Imaging Spectroscopy (IBIS). A wavelength tunable multi-chip infrared quantum cascade laser (QCL) is used to interrogate a surface while an MCT focal plane array (FPA) collects backscattered images to comprise a hyperspectral image (HSI) cube. The HSI cube is processed and the extracted spectral information is fed into an algorithm to detect and identify chemical traces. The machine learning algorithm utilizes a 1-dimensional convolutional neural network (CNN) that has been trained on augmented FTIR diffuse reflectance spectra. In this manuscript, we implement a 1-D CNN to identify chemicals within an IBIS hypercube. This demonstrates a form of active chemical imaging where the CNN identifies a chemical within each pixel of an IBIS hypercube. Chemical imaging capability goes beyond point detection and identification to indicate where each chemical is within the field of view, as well as identifying multiple target chemicals simultaneously.

Conference Presentation

Citation Download Citation

Christopher A. Kendziora, Eric Yao, Drew Finton, Drew C. Kendziora, Christopher J. Breshike, and Robert Furstenberg "Machine learning algorithms for standoff detection of threat chemicals by active infrared backscatter hyperspectral imaging", Proc. SPIE 12519, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXIX , 125190C (13 June 2023); https://doi.org/10.1117/12.2663065

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