Unsupervised SAR representation learning improves classification performance

Nolan Vaughn; Bo Sullivan; Kristen Jaskie

doi:10.1117/12.3013982

7 June 2024 Unsupervised SAR representation learning improves classification performance

Nolan Vaughn, Bo Sullivan, Kristen Jaskie

Proceedings Volume 13039, Automatic Target Recognition XXXIV; 130390J (2024) https://doi.org/10.1117/12.3013982
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

We compare the effectiveness of using a trained-from-scratch, unsupervised deep generative Variational Autoencoder (VAE) model as a solution to generic representation learning problems for Synthetic Aperture Radar (SAR) data as compared to the more common approach of using an Electric Optical (EO) transfer learning method. We find that a simple, unsupervised VAE training framework outperforms an EO transfer learning model at classification.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Nolan Vaughn, Bo Sullivan, and Kristen Jaskie "Unsupervised SAR representation learning improves classification performance", Proc. SPIE 13039, Automatic Target Recognition XXXIV, 130390J (7 June 2024); https://doi.org/10.1117/12.3013982

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