SAR层析成像利用多航过复数据对观测目标进行高程向重构，全极化数据具有丰富的散射信息。将全极化数据与SAR层析成像相结合，利用城市建筑高程向散射体的稀疏性和全极化数据信号稀疏支撑集相同的特点，提出基于组稀疏约束和稀疏约束相结合的求解模型，并利用层次稀疏的方法对模型进行求解。通过Monte Carlo仿真实验将该模型法的性能与单极化层析成像模型和基于组稀疏的求解方法的性能进行对比，并将该方法应用到实测数据的半点目标仿真实验中。结果表明，本文提出的方法提高了高程向重建精度，且有更好的鲁棒性，在低信噪比下也能较好地恢复目标的高程向信息和后向散射系数。
SAR tomography employs the multiple-pass data to achieve the elevation location reconstruction of the observation target, while the fully polarimetric data owns rich scattering information. We combine the full-polarimetric data with SAR tomography. By considering the same characteristic of the sparsity of elevation scatters in urban building and the sparse support set in full-polarimetric data, a solution model based on group sparse constraint and sparse constraint, solved by hierarchical sparse method, is proposed. The performance of the method has been compared with those of the single-polarimetric tomography model and the group sparse-based solution method by Monte Carlo simulation experiments. Meanwhile, the method is also applied to semi-simulation of point target experiments based on real data. The results show that the proposed method improves the accuracy of elevation reconstruction and has better robustness, and it accurately recovers the elevation position and backscatter coefficient of the target at low SNR.
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