在建筑密集的城区复杂场景中，高分辨率SAR影像中存在严重的叠掩效应，影像解译的难度加大。SAR层析成像可以分离单个分辨单元内混叠的散射体目标，并且获取各个散射体的3维位置和后向散射强度。该文首先论述了3维SAR层析成像的基本原理，针对传统谱估计法获得的高程向分辨率较低的问题，综述了压缩感知方法在城区3维SAR层析成像中的应用，以基追踪和双步迭代收缩阈值法为例，开展了TerraSAR-X聚束模式数据实验，并与传统的奇异值阈值法进行了对比分析。研究结果表明压缩感知方法的高程向超分辨率、旁瓣抑制优势明显，在城区SAR层析成像中具有广阔的应用前景。%In modern high resolution SAR data, due to the intrinsic side-looking geometry of SAR sensors, layover and foreshortening issues inevitably arise, especially in dense urban areas. SAR tomography provides a new way of overcoming these problems by exploiting the back-scattering property for each pixel. However, traditional non-parametric spectral estimators, e.g. Truncated Singular Value Decomposition (TSVD), are limited by their poor elevation resolution, which is not comparable to the azimuth and slant-range resolution. In this paper, the Compressive Sensing (CS) approach using Basis Pursuit (BP) and TWo-step Iterative Shrinkage/Thresholding (TWIST) are introduced. Experimental studies with real spotlight-mode TerraSAR-X dataset are carried out using both BP and TWIST, to demonstrate the merits of compressive sensing approaches in terms of robustness, computational efficiency, and super-resolution capability.
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