An efficient algorithm for compressive sensing based SAR tomography

Modern Tomographic SAR is an advanced InSAR techniques for urban mapping, which can not only retrieve 3D spatial information but also assess the 4D temporal information, such as deformation. To retrieve the information from InSAR data, several algorithms have been developed. Among them, SL1MMER algorithms is state of the art. However, it suffers from the computational expenses and it is hard to extend to large scale practice. In this work, we propose a novel optimization algorithms for L1 regularized least square in SL1MMER, which can keep the accuracy of the optimization result and dramatically speed up the processing.

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