Physics-guided neural networks for hyperspectral target identification

Abstract

We present results comparing black-box and physics-guided neural network architectures for hyperspectral target identification. Specifically, our physics-guided neural networks operate on at-sensor overhead long-wave infrared hyperspectral imaging radiances to predict not only the material class, but also physically-meaningful quantities of interest, such as the atmospheric transmission factor, the temperature, and the underlying material emissivity. In this way, our models are decoupled from traditional preprocessing routines and provide independently verifiable and interpretable quantities alongside the class predictions. We compare our physics-guided models to more traditional black-box models with respect to classification accuracy and representational similarity, and assess performance in predicting physical quantities across a variety of training schemes.