The classification of trace chemical residues through active spectroscopic sensing is challenging due to the lack of physics-based models that can accurately predict spectra. To overcome this challenge, we leveraged the field of domain adaptation to translate data from the simulated to the measured domain for training a classifier. We developed the first 1D conditional generative adversarial network (GAN) to perform spectrum-to-spectrum translation of reflectance signatures. We applied the 1D conditional GAN to a library of simulated spectra and quantified the improvement in classification accuracy on real data using the translated spectra for training the classifier. Using the GAN-translated library, the average classification accuracy increased from 0.622 to 0.723 on real chemical reflectance data, including data from chemicals not included in the GAN training set.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Department, Program, or Center
Chester F. Carlson Center for Imaging Science (COS)
Murphy, Cara and Kerekes, John, "1D Conditional Generative Adversarial Network for Spectrum-to-spectrum Translation of Simulated Chemical Reflectance signatures" (2021). Journal of Spectral Imaging, 10 (), 1-11. Accessed from
RIT – Main Campus