Sar automatic target recognition with less labels

Synthetic-Aperture-Radar (SAR) is a commonly used modality in mission-critical remote-sensing applications, including battlefield intelligence, surveillance, and reconnaissance (ISR). Processing SAR sensory inputs with deep learning is challenging because deep learning methods generally require large training datasets and high- quality labels, which are expensive for SAR. In this paper, we introduce a new approach for learning from SAR images in the absence of abundant labeled SAR data. We demonstrate that our geometrically-inspired neural architecture, together with our proposed self-supervision scheme, enables us to leverage the unlabeled SAR data and learn compelling image features with few labels. Finally, we show the test results of our proposed algorithm on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset.

Duke Scholars

Author Navid NaderiAlizadeh Biostatistics & Bioinformatics, Division of Integrative Geno ...