Synthetic data, measured data integrated learning experiments
Jeremy Cavallo, Edmund G. Zelnio · 2023
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.