Multi-Platform EO and SAR Fusion for Target ID

Electro-optical (EO) sensors and synthetic aperture radars (SAR) have long been used in automatic target recognition (ATR) classifiers. However, maintaining robust target ID performance with ATRs trained on data from a single EO or SAR sensor across a wide range of operating conditions is challenging due to the variability in the sun illumination and surface orientation of targets, respectively. Fusion of ATR classifiers trained on EO and SAR data can improve target ID performance. In this paper, we implement multiple fusion algorithms at the decision level and the feature level using classifiers trained on a standard convolutional neural network (CNN) architecture. Under favorable conditions - when the train and test data have the same operating conditions many fusion algorithms offer significant gain at lower image resolution compared to a single sensor at higher resolution. However, as the operating conditions become more disjoint, only the most robust fusion techniques show significant performance gain. A feature-level fusion algorithm developed by researchers at Sandia National Laboratories and modified for use in this paper showed the most robustness to disjoint operating conditions. Moreover, this algorithm was robust to geometric angle variation and therefore could be used with sensors mounted on different platforms.