The classifcaton of trace chemical residues through actve spectroscopic sensing is challenging due to the lack of physics-based models that can accurately predict spectra. To overcome this challenge, we leveraged the feld of domain adaptaton to translate data from the simulated to the measured domain for training a classifer. We developed the frst 1D conditonal generatve adversarial network (GAN) to perform spectrum-to-spectrum translaton of refectance signatures. We applied the 1D conditonal GAN to a library of simulated spectra and quantfed the improvement in classifcaton accuracy on real data using the translated spectra for training the classifer. Using the GAN-translated library, the average classifcaton accuracy increased from 0.622 to 0.723 on real chemical refectance data, including data from chemicals not included in the GAN training set.

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Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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Chester F. Carlson Center for Imaging Science (COS)


RIT – Main Campus