Benchmarking transferability metrics for SAR ATR

The lack of large, relevant and labeled datasets for synthetic aperture radar (SAR) automatic target recognition (ATR) poses a challenge for deep neural network approaches. In the case of SAR ATR, transfer learning offers promise where models are pre-trained on either synthetic SAR, alternatively collected SAR, or non-SAR source data and then fine-tuned on a smaller target SAR dataset. The concept being that the neural network can learn fundamental features from the more abundant source domain resulting in high accuracy and robust models when fine-tuned on a smaller target domain. One open question with this transfer learning strategy is how to choose source datasets that will improve accuracy of a target SAR dataset when the model is fine-tuned. Here, we apply a set of model and dataset transferability analysis techniques to investigate the efficacy of transfer learning for SAR ATR. In particular, we examine Optimal Transport Dataset Distance (OTDD), Log Maximum Evidence (LogMe), Log Expected Empirical Prediction (LEEP), Gaussian Bhattacharyya Coefficient (GBC), and H-Score. These methods consider properties such as task relatedness, statistical analysis of learned embedding properties, as well as distribution distances of the source and target domains. We apply these transferability metrics to ResNet18 models trained on a set of Non-SAR as well as SAR datasets. Overall, we present an investigation into quantitatively analyzing transferability for SAR ATR.