Sar image generation by integrating differentiable sar renderer with neural networks

Synthetic Aperture Radar (SAR) is extensively employed in both civilian and military sectors, with recent advancements leveraging deep learning for automatic SAR image interpretation. However, the effectiveness of these techniques, particularly Convolutional Neural Networks (CNN), is challenged by insufficient angle range in actual samples due to satellite incident angle constraints. This article proposes a method for generating multi-view samples of SAR targets based on a CNN module integrated with Differentiable SAR Renderer (DSR). Specifically, a polygon mesh is reconstructed from two-dimensional (2D) SAR images through the CNN module, and the DSR is utilized to reversely render SAR target images of various viewpoints from the reconstructed mesh, including the samples used to match with original input 2D images. Then, the generated images is used to compute the loss in training phase, and no three-dimensional (3D) ground truth is required. Experiments are conducted on simulated SAR images and the results demonstrate the efficacy of multi-view sample generation for SAR targets.