A Comparison of Data Augmentation Methods for Training Deep Neural Networks on Synthetic Aperture Sonar

תקציר מקורי באנגליתarXiv:2607.23770v1 Announce Type: new Abstract: In this work we study Automatic Target Recognition (ATR) for Synthetic Aperture Sonar (SAS) data with a focus on deep neural networks (DNNs). The main challenge in training DNNs for SAS ATR arises from the limited quantity of labeled target examples, which arises due to the significant costs and time required to collect real-world SAS data. One successful general strategy for mitigating the problem of limited training data is augmentation, which generates additional synthetic training data by introducing realistic variations to available real-world data. Prior research has investigated a variety of augmentation strategies for SAS ATR, including conventional image augmentations (e.g., contrast changes, cropping) as well as physics-based augmen