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.

我们提出了一个跨模态重建网络（CMR-Net），它通过将可微渲染和光学图像的交叉模态监督整合在一起，将高度稀疏的多基线 SAR 3D 目标图像重建为结构化和高分辨率的图像。该网络通过精心设计的网络架构和训练策略，能够在公开可用的仿真数据集和真实测量数据集上实现高分辨率的重建，优于基于压缩感知和其他基于学习的传统稀疏重建算法。此外，使用光学图像作为监督提供了一种经济高效的方式来构建训练数据集，降低了方法传播的难度。我们的工作展示了在多基线 SAR 3D 成像中深度学习的广泛前景，并为基于交叉模态学习理论的雷达成像研究提供了一条新的路径。