SAR-to-Virtual Optical Image Translation for Improving SAR Automatic Target Recognition

This letter addresses the challenges associated with interpreting synthetic aperture radar (SAR) images, which provide limited visual information compared with optical images. We propose a new method for generating virtual data and an SAR-to-optical image translation neural network to recognize targets in SAR images. The creation of virtual data involves classifying it into targets and backgrounds. The target data generate a virtual optical image using a 3-D model, while the background data are synthesized by combining the target with the existing SAR image. For image translation using the virtual dataset, we design a modified dense nested U-net that converts images for target recognition. By incorporating the proposed translation network into the YOLO v4 detection algorithm, we verify the impact of virtual optical images on target recognition. The experimental results demonstrate that our proposed method outperforms the conventional approach, which relies solely on SAR target image data for learning.