Spectral Data Augmentation Using Deep Generative Model for Remote Chemical Sensing

The critical role of a remote chemical sensing using a Fourier Transform Infrared (FT-IR) spectrometer has been emphasized for detecting lethal chemicals in the atmosphere. To enhance standoff detection capabilities, acquiring adequate gas spectral data is crucial for training and optimizing detection algorithms across diverse outdoor scenarios. However, the collection of outdoor infrared spectra with a number of conditions is constrained owing to uncontrolled weather factors including a temperature and humidity, leading to impaired reliability of the data. In addressing outdoor data acquisition challenges, we introduced a data augmentation method using a conditional CycleGAN. This technique utilizes spectral data obtained exclusively under controlled laboratory conditions. The proposed deep generative model takes as input the background spectrum, which is concatenated with two critical attributes: the temperature difference between the target substance and the background, and pathlength concentration. Subsequently, the model computes a brightness temperature spectrum for a gas against a specific background, employing SF6 as the target chemical gas. The validity of the generated data was assessed using two detection algorithms: the Pearson Correlation Coefficient and Adaptive Subspace Detector. In addition, the accuracy performance of detectors trained with the augmented dataset was compared and evaluated against those trained with the pure dataset. The results demonstrated that the model can simulate gas spectra onto unseen background spectra and enhance the chemical sensing database, and it can contribute to data augmentation for improving the performance of chemical gas detection systems.