Intelligent radar framework published by HRL

MALIBU, Calif. Scientists at HRL Laboratories have published their new framework for training computer deep neural networks to be able to classify synthetic aperture radar (SAR) images without a large labeled data set, solving the problem of SAR image identification when only a few labeled data were available.

Photo courtesy of HRL Laboratories.

The system uses a shared learning space between a large labeled electro-optical (EO) dataset and the SAR system. Both systems learn to transfer data within the shared space, which enables the SAR system to classify data from the EO system without having a label on each data element.

SAR is usually installed in moving vehicles such as aircraft. By scanning over objects, SAR creates two-dimensional images or three-dimensional reconstructions of objects, such as landscapes.