A Novel Fast Approach for SAR Tomography: Two-Step Iterative Shrinkage/Thresholding

As an advanced technique, synthetic aperture radar (SAR) tomography makes it possible to overcome the layover problem induced by the intrinsic side-looking geometry of SAR sensors. However, traditional nonparametric spectral estimators, e.g., truncated singular value decomposition, are limited by their poor elevation resolution. On the other hand, the compressive-sensing-based approaches using the basis-pursuit strategy to find an L1-norm minimization solution for SAR tomography (TomoSAR) are extremely time consuming. Therefore, a fast and robust tomographic algorithm with super-resolution capability is needed. In this letter, we propose a new approach for TomoSAR based on two-step iterative shrinkage/thresholding (TWIST). TWIST uses a two-step strategy to speed up the L1-norm minimization procedure and can achieve an exceptionally fast convergence speed for TomoSAR. Experimental studies with simulated signals and a spotlight-mode TerraSAR-X data set were carried out to demonstrate the merits of the proposed TWIST approach in terms of robustness, fast convergence speed, and super-resolution capability.