Synthetic data, measured data integrated learning experiments

Jeremy Cavallo; Edmund Zelnio

doi:10.1117/12.2665521

13 June 2023 Synthetic data, measured data integrated learning experiments

Jeremy Cavallo, Edmund Zelnio

Proceedings Volume 12520, Algorithms for Synthetic Aperture Radar Imagery XXX; 125200N (2023) https://doi.org/10.1117/12.2665521
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

This paper addresses the problem of adequately training deep learning networks to be operational on measured Synthetic Aperture Radar (SAR) data when the quantity of measured data alone is insufficient. In particular, this is a study in transfer learning utilizing synthetically generated SAR data and measured SAR data to train a deep learning algorithm to classify military tactical vehicles. The present study is motivated by sparsity of measured data for Air Force targets of interest. Specifically, this effort builds on an existing Convolution Neural Network (CNN) architecture, i.e. Understanding the Synthetic and Measured GAP from the CNN Classifier Perspective and aims to improve achievable performance by increasing the algorithm complexity and performing parameter analysis on MSTAR data.

Citation Download Citation

Jeremy Cavallo and Edmund Zelnio "Synthetic data, measured data integrated learning experiments", Proc. SPIE 12520, Algorithms for Synthetic Aperture Radar Imagery XXX, 125200N (13 June 2023); https://doi.org/10.1117/12.2665521

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KEYWORDS

Data modeling

Synthetic aperture radar

Performance modeling

Network architectures

Air force

Deep learning

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