Abstract
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.
Highlights
Identifying trace amounts (≤200 μg cm−2) of chemicals on surfaces is a desirable capability for a wide range of defence, intelligence and law enforcement applications.[1]
The average ratios of correct and incorrect chemical predictions are shown in Figure 3 for each classifier training method: using the library simulated by the sparse transfer matrix (STM) model and translated by the generator portion of the generative adversarial network (GAN)
We suggest that though the model is best-suited for modelling chemical residue phenomenology, there are some limitations in its ability to fit to real data
Summary
Identifying trace amounts (≤200 μg cm−2) of chemicals on surfaces is a desirable capability for a wide range of defence, intelligence and law enforcement applications.[1] Chemicals of interest for these applications include explosives, chemical warfare agents, narcotics etc. The system operates by measuring the spectral reflectance, or the portion of which is reflected back towards the sensor, of the target surface in the LWIR portion of the optical spectrum using quantum cascade lasers (QCL) as the illumination source[3,5,6] and comparing the measured signature to a spectral library of reference signatures.
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