Hyperspectral Image Target Detection Using Deep Ensembles for Robust Uncertainty Quantification

Deep learning (DL) has been widely proposed for target detection in hyperspectral image (HSI) data. Yet, standard DL models produce point estimates at inference time, with no associated measure of uncertainty, which is vital in high-consequence HSI applications. In this work, we develop an uncertainty quantification (UQ) framework using deep ensemble (DE) learning, which builds upon the successes of DL-based HSI target detection, while simultaneously providing UQ metrics. Specifically, we train an ensemble of convolutional deep learning detection models using one spectral prototype at a particular time of day and atmospheric condition. We find that our proposed framework is capable of accurate target detection in additional atmospheric conditions and times of day despite not being exposed to them during training. Furthermore, in comparison to Bayesian Neural Networks, another DL based UQ approach, we find that DEs provide increased target detection performance while achieving comparable probabilities of detection at constant false alarm rates.