查看文章

Electromagnetic imaging methods mainly utilize converted sampling, dimensional transformation, and coherent processing to obtain spatial images of targets, which often suffer from accuracy and efficiency problems. Deep neural network (DNN)-based high-resolution imaging methods have achieved impressive results in improving resolution and reducing computational costs. However, previous works exploit single modality information from electromagnetic data; thus, the performances are limited. In this article, we propose an electromagnetic image generation network (EMIG-Net), which translates electromagnetic data of multiview 1-D range profiles (1DRPs), directly into bird-view 2-D high-resolution images under cross-modal supervision. We construct an adversarial generative framework with visual images as supervision to significantly improve the imaging accuracy. Moreover, the network structure is carefully …