SAR automatic target recognition with less labels

Joseph F. Comer; Reed W. Andrews; Navid Naderializadeh; Soheil Kolouri; Heiko Hoffman

doi:10.1117/12.2564875

24 April 2020 SAR automatic target recognition with less labels

Joseph F. Comer, Reed W. Andrews, Navid Naderializadeh, Soheil Kolouri, Heiko Hoffman

Proceedings Volume 11394, Automatic Target Recognition XXX; 113940Q (2020) https://doi.org/10.1117/12.2564875
Event: SPIE Defense + Commercial Sensing, 2020, Online Only

Abstract

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.

Conference Presentation

Citation Download Citation

Joseph F. Comer, Reed W. Andrews, Navid Naderializadeh, Soheil Kolouri, and Heiko Hoffman "SAR automatic target recognition with less labels", Proc. SPIE 11394, Automatic Target Recognition XXX, 113940Q (24 April 2020); https://doi.org/10.1117/12.2564875

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