Blending synthetic and measured data using transfer learning for synthetic aperture radar target classification

Convolutional neural networks (CNNs) are state-of-the-art techniques for image classification; however, CNNs require an extensive amount of training data to achieve high accuracy. This demand presents a challenge because the existing amount of measured synthetic aperture radar (SAR) data is typically limited to just a few examples and does not account for articulations, clutter, and other target or scene variability. Therefore, this research aimed to assess the feasibility of combining synthetic and measured SA 13 images to produce a classification network that is robust to operating conditions not present in measured data and that may adapt to new targets without necessarily training on measured SA I? images. A network adapted from the CIFAR-10 LeNet architecture in Ni ATLA 13. Convolutional Neural Network (MatCorivNet.) was first trained on a database of multiple synthetic Moving and Stationary Target Acquisition and Recognition (MSTAR) targets. After the network classified with almost perfect accuracy, the synthetic data was replaced with corresponding measured data. Only the first layer of filters was permitted to change in order to create a translation layer between synthetic and measured data. The low error rate of this experiment demonstrates that diverse clutter and target types not represented in measured training data may be introduced in synthetic training data and later recognized in measured test data.