Sparse Multi-baseline SAR Cross-modal 3D Reconstruction of Vehicle Targets

Multi-baseline SAR 3D imaging faces significant challenges due to data sparsity. In recent years, deep learning techniques have achieved notable success in enhancing the quality of sparse SAR 3D imaging. However, previous work typically rely on full-aperture high-resolution radar images to supervise the training of deep neural networks (DNNs), utilizing only single-modal information from radar data. Consequently, imaging performance is limited, and acquiring full-aperture data for multi-baseline SAR is costly and sometimes impractical in real-world applications. In this paper, we propose a Cross-Modal Reconstruction Network (CMR-Net), which integrates differentiable render and cross-modal supervision with optical images to reconstruct highly sparse multi-baseline SAR 3D images of vehicle targets into visually structured and high-resolution images. We meticulously designed the network architecture and training strategies to enhance network generalization capability. Remarkably, CMR-Net, trained solely on simulated data, demonstrates high-resolution reconstruction capabilities on both publicly available simulation datasets and real measured datasets, outperforming traditional sparse reconstruction algorithms based on compressed sensing and other learning-based methods. Additionally, using optical images as supervision provides a cost-effective way to build training datasets, reducing the difficulty of method dissemination. Our work showcases the broad prospects of deep learning in multi-baseline SAR 3D imaging and offers a novel path for researching radar imaging based on cross-modal learning theory.