Multi-baseline Synthetic Aperture Radar (SAR) three-dimensional (3D) tomography is a crucial remote sensing technique that provides 3D resolution unavailable in conventional SAR imaging. However, achieving high-quality imaging typically requires multi-angle or full-aperture data, resulting in significant imaging costs. Recent advancements in sparse 3D SAR, which rely on data from limited apertures, have gained attention as a cost-effective alternative. Notably, deep learning techniques have markedly enhanced the imaging quality of sparse 3D SAR. Despite these advancements, existing methods primarily depend on high-resolution radar images for supervising the training of deep neural networks (DNNs). This exclusive dependence on single-modal data prevents the introduction of complementary information from other data sources, limiting further improvements in imaging performance. In this paper, we introduce a Cross-Modal 3D-SAR Reconstruction Network (CMAR-Net) to enhance 3D SAR imaging by integrating heterogeneous information. Leveraging cross-modal supervision from 2D optical images and error transfer guaranteed by differentiable rendering, CMAR-Net achieves efficient training and reconstructs highly sparse multi-baseline SAR data into visually structured and accurate 3D images, particularly for vehicle targets. Extensive experiments on simulated and real-world datasets demonstrate that CMAR-Net significantly outperforms SOTA sparse reconstruction algorithms based on compressed sensing (CS) and deep learning (DL). Furthermore, our method eliminates the need for time-consuming full-aperture data preprocessing and relies solely on computer-rendered optical images, significantly reducing dataset construction costs. This work highlights the potential of deep learning for multi-baseline SAR 3D imaging and introduces a novel framework for radar imaging research through cross-modal learning.

翻译：多基线合成孔径雷达（SAR）三维层析成像是一种关键的遥感技术，它提供了传统SAR成像所不具备的三维分辨能力。然而，实现高质量成像通常需要多角度或全孔径数据，导致成像成本高昂。近年来，依赖于有限孔径数据的稀疏三维SAR技术作为一种经济高效的替代方案受到关注。值得注意的是，深度学习技术显著提升了稀疏三维SAR的成像质量。尽管取得了这些进展，现有方法主要依赖高分辨率雷达图像来监督深度神经网络（DNN）的训练。这种对单一模态数据的依赖阻碍了从其他数据源引入互补信息，限制了成像性能的进一步提升。本文提出了一种跨模态三维SAR重建网络（CMAR-Net），通过整合异构信息来增强三维SAR成像。CMAR-Net利用来自二维光学图像的跨模态监督以及由可微分渲染保证的误差传递，实现了高效训练，并将高度稀疏的多基线SAR数据重建为视觉结构清晰且精确的三维图像，尤其适用于车辆目标。在模拟和真实数据集上进行的大量实验表明，CMAR-Net显著优于基于压缩感知（CS）和深度学习（DL）的最先进稀疏重建算法。此外，我们的方法无需耗时的全孔径数据预处理，仅依赖于计算机渲染的光学图像，显著降低了数据集构建成本。这项工作凸显了深度学习在多基线SAR三维成像中的潜力，并通过跨模态学习为雷达成像研究引入了一个新颖的框架。