Classification of Non-Referenced Continuous-Wave Terahertz Reflection Spectra for Remote Material Identification

Mathias Hedegaard Kristensen*, Pawel Piotr Cielecki, Esben Skovsen

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1 Citationer (Scopus)
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Abstract

Commonly, terahertz spectra (both continuous-wave and pulsed) are deconvoluted by reference spectra to remove the water vapor absorption lines and other system related responses. However, in real-life applications obtaining reference spectra can be problematic and adds to the complexity of the system. Thus, a reference-free method for classification of terahertz spectra could be a welcomed advance for remote sensing applications. In this paper, we study how simple machine learning algorithms perform as a reference-free method for terahertz stand-off identification of materials. The algorithms are trained using spectra measured under controlled humidity conditions and tested by a completely independent data set measured under ambient conditions. We apply three different classification algorithms; namely a Gaussian Bayes model, the k nearest neighbors, and a support vector machine. We found that, if the terahertz spectra are processed using a supervised algorithm (Regularized Linear Discriminant Analysis), very high classification scores (¿98.6%) can be retained for the non-referenced spectra. Moreover, the high accuracy is obtained meanwhile the dimensionality is reduced by a factor larger than 160, which further reduces the computational requirements. Hence, we have demonstrated that simple supervised machine learning algorithms can serve as a highly accurate reference-free method for THz material identification. This could be of great importance for real-world remote sensing applications based on terahertz spectroscopy.

OriginalsprogEngelsk
Artikelnummer105420
TidsskriftInfrared Physics and Technology
Vol/bind140
ISSN1350-4495
DOI
StatusUdgivet - aug. 2024

Emneord

  • Classification algorithms
  • Reflection spectroscopy
  • Remote sensing
  • Terahertz (THz)

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