Optimizing machine learning algorithms for hyperspectral imaging and trace detection

Andrew Kendziora; Christopher J. Breshike; Robert Furstenberg; Tyler Huffman; Christopher A. Kendziora

doi:10.1117/12.3013984

7 June 2024 Optimizing machine learning algorithms for hyperspectral imaging and trace detection

Andrew Kendziora, Christopher J. Breshike, Robert Furstenberg, Tyler Huffman, Christopher A. Kendziora

Proceedings Volume 13031, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX; 130310K (2024) https://doi.org/10.1117/12.3013984
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

In support of the detection of explosives and threat chemicals by active infrared backscatter hyperspectral imaging, we are training algorithms to process and alert on possible threats. Surfaces are interrogated using infrared quantum cascade lasers (QCL) and the backscattered signal is collected using a cooled MCT focal plane array (FPA). The QCLs can tune across their full wavelength range, from 6 – 11 m, in less than one second. Full 128 X 128 pixel frames from the FPA are collected and compiled into a hyperspectral image (HSI) cube containing spectral and spatial information from the target. The HSI cubes are processed and the spectra from extracted pixel locations are then run through an algorithm to detect and identify traces of explosives. We train our algorithms on both synthetic and experimental data. In this presentation, we utilize machine learning algorithms to classify HSI cubes from a series of targets coupons fabricated on relevant substrates (glass, painted metal, plastics, cardboard). We explain how the algorithm training uses reference spectral measurements from our cart system as well as from a benchtop FTIR. The generation and utility of synthetic data is described regarding how we populate the algorithms’ spectral library more densely than would be possible using only measured experimental data. The performance of several ML algorithms is described.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Andrew Kendziora, Christopher J. Breshike, Robert Furstenberg, Tyler Huffman, and Christopher A. Kendziora "Optimizing machine learning algorithms for hyperspectral imaging and trace detection", Proc. SPIE 13031, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX, 130310K (7 June 2024); https://doi.org/10.1117/12.3013984

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