| Autor: |
Tyler J. Huffman, R. Andrew McGill, Christopher A. Kendziora, Drew C. Kendziora, Robert Furstenberg, Drew M. Finton, Christopher J. Breshike |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII. |
| DOI: |
10.1117/12.2585982 |
| Popis: |
We are developing algorithms to identify chemicals of interest by their diffuse infrared (IR) reflectance signatures when they are deposited as particles on surfaces. 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 algorithm utilizes a convolutional neural network (CNN) that has been pre-trained on synthetic diffuse reflectance spectra. In this manuscript, we present an approach to generate large libraries of synthetic infrared reflectance spectra for use in training and testing the CNN. We demonstrate advancements in the number of analytes, a method to generate synthetic substrate spectra, and the benefits of subtracting the substrate “background” to train and test the CNN on the resulting differential spectra. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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