Synthetic forward-looking infrared signatures for training and testing target identification classifiers

Abstract

A series of experiments are performed to benchmark the performance of a target identification classifier trained on synthetic forward-looking infrared (FLIR) target signatures. Results show that the classifier, when trained on synthetic target signatures and tested on measured, real-world target signatures, can perform as well as when trained on measured target signatures alone. It is also shown that when trained on a combined database of measured plus synthetic target signatures, performance exceeds that when trained on either database alone. Finally, it is shown that within a large, diverse database of signatures there exists a subset of signatures whose trained classifier performance can exceed that achieved using the whole database. These results suggest that for classification applications, synthetic FLIR data can be used when enough measured data is unavailable or cannot be obtained due to expense or unavailability of targets, sensors, or site access.