Weighted residual network for SAR automatic target recognition with data augmentation - Li, Junyu; Peng, Cheng (Frontiers in Neurorobotics, 2023)
Decades of research have been dedicated to addressing the challenges of synthetic aperture radar (SAR) automatic target recognition (ATR). Recently, deep learning algorithms have gained significant attention in this field, yielding promising outcomes. Nonetheless, several issues remain unresolved, such as the impact of SAR image noise, the need for extensive training datasets, and the time-consuming nature of training learning models. These challenges have motivated us to propose a data augmentation scheme in this study. By introducing and subsequently canceling speckle noise, we expand the ground-truth dataset using noise perturbation. Building upon this foundation, we developed a weighted ResNet with residual strain control, leveraging the desirable qualities of ResNet, such as low computational requirements and reduced training data needs. This approach aims to shorten the training time and enhance SAR ATR performance.Experimental results validate that the weighted ResNet, coupled with the proposed SAR image data augmentation technique, achieves superior computational efficiency and recognition accuracy compared to existing approaches that have been tested.