本文为德国柏林工业大学的博士论文，共146页。

合成孔径雷达（SAR）是一种有源微波仪器，能够在白天/夜晚和全天候条件下以特定波长和偏振对地球表面进行成像。在它的基本配置中，一个沿直线轨迹运行的小型机载/星载天线以侧视方式指向垂直于飞行轨迹的方向。由此能够虚拟合成沿轨道天线的孔径，从而形成辐射区域的高分辨率二维图像。此外，当考虑具有交叉轨迹和/或仰角位移的多条平行轨迹时，由此产生的传感几何结构使得能够合成两个允许三维后向散射剖面的虚拟天线孔径。这种成像方式被称为SAR层析成像，通常通过首先获得多个二维共配准SAR图像来实现，使得每个图像对应于一个平行过程，然后采用一维标准谱估计技术。

一个典型的应用是植被区域的三维成像，由于长波辐射的高穿透能力，已被证明对森林结构的估计以及对地上生物量的量化具有重要价值。此外，随着长波星载雷达的出现，层析合成孔径雷达技术将引起相当大的兴趣，因为层析数据集将得到大规模获取。然而，理想的采样条件需要大量的密集规则采集，这不仅有限且昂贵，而且会导致时间去相关。

本文探讨了在稀疏驱动的压缩感知（CS）框架下，减少森林地区三维SAR成像所需穿越次数的可能性。为此，上述基本上产生垂直后向散射剖面的1-D谱估计步骤将被视为挑选欠定线性系统的解的过程。在这方面，标准将是基于选择的后向散射剖面，使得其可以稀疏地表示在替代域中。特别是，使用小波基将被证明是一个合适的选择。该方法适用于单通道和极化传感器，对非理想采集具有鲁棒性，并能确保物理有效性。此外，这些基于稀疏性的技术将作为传感器到目标距离、所需先验知识和计算时间的函数进行评估。此外，本文还介绍了一种分离森林散射机制的凸优化方法。实质上，该方法的目的是对层析数据集进行预滤波，以便能够分别重建冠层和地面。最后，将使用德国航空航天中心（DLR）的实验SAR（E-SAR）传感器获得的极化L波段和P波段数据进行全面验证。

A synthetic aperture radar (SAR) is an active microwave instrument capable of imaging the surface of the earth at specific wavelengths and polarizations in day/night and all-weather conditions. In its basic configuration, a small airborne/spaceborne antenna traveling along a straight-line trajectory is pointed perpendicular to the flight track in a side-looking fashion. This results in the synthesis of a virtual along-track antenna aperture that enables the formation of a high-resolution 2-D image of the illuminated area. Moreover, when multiple parallel trajectories—with cross-track and/or elevation displacements—are considered, the resulting sensing geometry enables the synthesis of two virtual antenna apertures that allow for 3-D backscatter profiling. This imaging modality is known as SAR tomography and is commonly approached by first obtaining multiple 2-D coregistered SAR images—such that each image corresponds with a parallel pass—followed by 1-D standard spectral estimation techniques. A typical application is the 3-D imaging of vegetated areas which, due to the high-penetration capabilities of radiation at long wavelengths, has proven to be of great value for the estimation of forest structure and, in turn, for the quantification of above ground biomass. In addition, with the anticipated advent of long-wavelength spaceborne radars, tomographic SAR techniques will become of considerable interest, as tomographic data sets will be available on a large scale. However, ideal sampling conditions are known to require a large number of dense regular acquisitions, which are not only limited and expensive but can also lead to temporal decorrelation. This dissertation explores the possibility of reducing the number of passes required for 3-D SAR imaging of forested areas by formulating the problem in a sparsity-driven framework usually referred to as compressed sensing (CS). To this end, the aforementioned 1-D spectral estimation step—which basically yields a vertical backscatter profile—will be regarded as the process of singling out a solution to an underdetermined linear system. In this regard, the criterion will be based on choosing a backscatter profile such that it can be sparsely represented in an alternative domain. In particular, the use of a wavelet basis will prove to be a suitable choice. The method will be formulated for both single-channel and polarimetric sensors and will be shown to be robust to nonideal acquisitions as well as to be able to ensure physical validity. Also, these sparsity-based techniques will be evaluated as a function of sensor-to-target distance, required a priori knowledge, and computation time. Furthermore, a convex optimization approach to separation of forest scattering mechanisms will be introduced. In essence, the method aims to pre-filter tomographic data sets so that canopy and ground contributions can be separately reconstructed. Finally, a thorough validation will be provided by using polarimetric L- and P-band data acquired by the Experimental SAR (E-SAR) sensor of the German Aerospace Center (DLR).

1.引言

2. SAR层析成像基础

3. 极化森林散射

4. 基于稀疏的SAR层析成像

5. 基于凸优化的SM分离

6. 层析成像的凸特性

7. 实验结果

8. 结论与建议

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