Investigations into out-of-domain performance of a two-step ATR based on a fusion of thermal and environmental data

Abstract

Automatic target recognition (ATR) algorithms that rely on machine learning approaches are limited by the quality of the training dataset and the out-of-domain performance. The performance of a two-step ATR algorithm (ATR-EnvI) that on fusing thermal imagery with environmental data is investigated using thermal imagery containing buried and surface object collected in New Hampshire, Mississippi, Arizona, and Panama. An autoencoder neural network is used to encode the salient environmental conditions for a given climatic condition into an environmental feature vector. The environmental feature vector allows for the inclusion of environmental data with varying dimensions to robustly treat missing data. Using this architecture, we evaluate the performance of the two-step ATR on a test dataset collected in an unseen climatic condition, e.g., tropical wet climate when the training dataset contains imagery collected in a similar condition, e.g., subtropical, and dissimilar climates. Lastly, we evaluate the impact of including physics-based synthetic training data has on performance for out-of-domain climates.