A Deep Neural Network Model for Hazard Classification

Hazard learning algorithms employing ground penetrating radar (GPR) data for purposes of discrimination, detection, and classification suffer from a pernicious robustness problem; models trained on a particular physical region using a given sensor (antenna system) typically do not transfer effectively to diverse regions interrogated with differing sensors. We implement a novel training paradigm using region-based stratified cross-validation that improves learning induction across disparate data sets. We test this training paradigm on a novel deep neueral network architecture (DNN) and report empirical results from testing/training on data collected from multiple sites. Furthermore, we discuss the relationship between penalty loss and evaluation metrics.