Towards SAR Tomographic Inversion via Sparse Bayesian Learning

AETomo-Net: A Novel Deep Learning Network for Tomographic SAR Imaging Based on Multi-dimensional Features

Tomographic synthetic aperture radar (TomoSAR) imaging algorithms based on deep learning can effectively reduce computational costs. The idea of existing researches is to reconstruct the elevation for each range-azimuth cell in one-dimensional using a deep-unfolding network. However, since these methods are commonly sensitive to signal sparsity level, it usually leads to some drawbacks like continuous surface fractures, too many outliers, \textit{et al}. To address them, in this paper, a novel imaging network (AETomo-Net) based on multi-dimensional features is proposed. By adding a U-Net-like …

Gridless Tomographic SAR Imaging Based on Accelerated Atomic Norm Minimization with Efficiency

Synthetic aperture radar (SAR) tomography (TomoSAR) enables the reconstruction and three-dimensional (3D) localization of targets based on multiple two-dimensional (2D) observations of the same scene. The resolving along the elevation direction can be treated as a line spectrum estimation problem. However, traditional super-resolution spectrum estimation algorithms require multiple snapshots and uncorrelated targets. Meanwhile, as the most popular TomoSAR imaging method in modern years, compressed sensing (CS) based methods suffer from the gridding mismatch effect which markedly degrades the i…

一种用于高效SMV无网格线谱估计的新型梯度下降最小二乘（GDLS）算法及其在层析SAR成像中的应用

A Novel Gradient Descent Least Squares (GDLS) Algorithm for Efficient SMV Gridless Line Spectrum Estimation with Applications in Tomographic SAR Imaging

本文提出GDLS算法，将SMV无网格线谱估计转化为最小二乘问题并用梯度下降迭代求解，以低于ANM的复杂度避免离网问题，并在TomoSAR成像中验证了性能。

$\boldsymbolγ$-Net: Superresolving SAR Tomographic Inversion via Deep Learning

Synthetic aperture radar tomography (TomoSAR) has been extensively employed in 3-D reconstruction in dense urban areas using high-resolution SAR acquisitions. Compressive sensing (CS)-based algorithms are generally considered as the state of the art in super-resolving TomoSAR, in particular in the single look case. This superior performance comes at the cost of extra computational burdens, because of the sparse reconstruction, which cannot be solved analytically and we need to employ computationally expensive iterative solvers. In this paper, we propose a novel deep learning-based super-resolv…

A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging

Deep learning (DL)-based tomographic SAR imaging algorithms are gradually being studied. Typically, they use an unfolding network to mimic the iterative calculation of the classical compressive sensing (CS)-based methods and process each range-azimuth unit individually. However, only one-dimensional features are effectively utilized in this way. The correlation between adjacent resolution units is ignored directly. To address that, we propose a new model-data-driven network to achieve tomoSAR imaging based on multi-dimensional features. Guided by the deep unfolding methodology, a two-dimension…

HyperLISTA-ABT：一种超轻展开网络用于准确的多成分差分雷达成像反演

HyperLISTA-ABT: An Ultra-light Unfolded Network for Accurate Multi-component Differential Tomographic SAR Inversion

本文提出HyperLISTA-ABT算法，通过分析方法确定权重，实现超轻量级模型，提升计算效率并保持性能，实验证明其在4D点云重建中表现优异。