Dynamic one-shot target detection and classification using a pseudo-Siamese network and its application to Raman spectroscopy

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Target detection and classification by Raman spectroscopy are important techniques for biological and chemical defense in military operations. Conventionally, these techniques preprocess the observed spectra using smoothing or baseline correction and apply detection algorithms like the generalized likelihood ratio test, independent component analysis, nonnegative matrix factorization, etc. These conventional detection algorithms need preprocessing and multiple shots of Raman spectra to get a reasonable accuracy. Recently, techniques based on deep learning are being used for target detection and classification due to its great adaptability and high accuracy over other methods and due to no requirement for preprocessing. Deep learning may give a good performance, but need retraining when untrained class targets are introduced which is time-consuming and bothersome. We devise a novel algorithm using a variant of the pseudo-Siamese network, one of the deep learning algorithms, that does not need retraining to detect and classify untrained class targets. Our algorithm detects and classifies targets with only one shot. In addition, our algorithm does not need preprocessing. We verify our algorithm with Raman spectra measured using a Raman spectrometer.
Publisher
ROYAL SOC CHEMISTRY
Issue Date
2021-11
Language
English
Article Type
Article
Citation

ANALYST, v.146, no.22, pp.6997 - 7004

ISSN
0003-2654
DOI
10.1039/d1an01352a
URI
http://hdl.handle.net/10203/289157
Appears in Collection
EE-Journal Papers(저널논문)
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