DUSG-Tomo-Net：一种用于非均匀基线下单视空间borne SAR Tomography超分辨率的深度解折叠神经网络
Geometry-Decoupled Deep Unfolding for Gridless Super-Resolution TomoSAR Under Nonuniform Baselines
AI总结：
本文提出DUSG-Tomo-Net，一种深度解折叠网格less框架，通过学习Toeplitz结构协方差表示，解决非均匀基线下空间borne SAR Tomography的超分辨率问题。
摘要：
合成孔径雷达成像（TomoSAR）通过多基线采集实现三维成像，已成为城市测绘的重要工具。为实现超分辨率反演，基于压缩感知（CS）的稀疏重建方法被广泛应用。然而，大多数基于CS的TomoSAR方法依赖于网格化公式，因此存在离网格偏差。网格化公式提供了一种减轻此限制的原理性方法，而经典Toeplitz-Vandermonde原子范数最小化（ANM）不直接适用于非均匀基线下的空间borne TomoSAR。现有的非均匀基线TomoSAR网格化方法避免了经典的均匀线性阵列（ULA）假设，但通常紧密耦合于手工迭代求解器和求解器特定的参数设置，而有限观测和低信噪比条件下的鲁棒反演仍具挑战性。为解决这一差距，我们提出DUSG-Tomo-Net，一种深度解折叠网格less框架，用于单视空间borne TomoSAR下的非均匀基线。所提出的方法通过结构化的单视近似将反演改写为Toeplitz兼容的滞后域，通过逐层学习正则化和基于投影的结构强制恢复Hermitian Toeplitz正半定结构的协方差表示。实际采集几何通过固定、信号无关的操作符嵌入到数据一致性步骤中，从而实现操作符基于的适应于变化的基线配置。散射体高度通过连续域频谱估计器估计，而无需高度离散化。
英文摘要：
Super-resolution SAR tomography (TomoSAR) is performed on a discretized elevation grid, leading to off-grid bias and spectral leakage. Classical Toeplitz-Vandermonde gridless formulations avoid elevation discretization but rely on uniform sampling, whereas covariance- or subspace-based estimation is difficult in single-look repeat-pass TomoSAR. We propose DUSG-Tomo-Net, a geometry-decoupled deep unfolding framework for gridless inversion under nonuniform baselines. In DUSG-Tomo-Net, pairwise products of a single observation vector are related to a latent Toeplitz-compatible virtual-lag sequence through an analytical geometry operator and modeled as noisy covariance surrogates containing multi-scatterer cross terms, measurement noise, and lag-interpolation errors. Each unfolded layer combines learned lag-domain regularization, closed-form geometry-dependent data consistency, and finite-step Dykstra projection toward the set of Hermitian Toeplitz positive semidefinite matrices. Root-MUSIC then retrieves scatterer elevations in the continuous domain without an elevation dictionary. Because the trainable modules operate only on the common virtual-lag representation, the learned parameterization can be reused across representationally compatible acquisition geometries by recomputing the geometry operator. At 6 dB, simulations under nonuniform baselines yield a single-scatterer RMSE of 0.703 m and an effective detection rate of approximately 0.90 for two scatterers at the Rayleigh limit, while demonstrating reliable sub-Rayleigh separation. A model trained with 20 acquisitions remains applicable to perturbed configurations containing 8-28 acquisitions without retraining. Experiments on a 16-image CH-1 stack show that DUSG-Tomo-Net produces coherent urban elevation maps and more continuous double-scatterer structures than the gridless baseline method TADCG.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
