The current synthetic aperture radar (SAR) images with ultrahigh-resolution provide detailed structures of the urban areas. Utilizing stacks of ultrahigh-resolution SAR images acquired with different view angles, tomographic SAR (TomoSAR) becomes an advanced technique to retrieve 3-D spatial information of the detailed structures which presents the efficient density in the point clouds. The technique is a sparse reconstruction problem indeed and can be solved by compressive sensing (CS) algorithms. However, conventional CS algorithms process the pixel independently and the detailed structures of the targets are easily lost followed by the sparsity constraints. In this article, we apply morphology regularization as a prior term to form a novel approach based on the CS algorithm. The morphology regularization enhances the detailed 3-D structural properties of targets, which can shrink the concatenations caused by outliers in the iterative reconstruction. As for the optimization algorithm for tomographic inversion, we apply the framework of the alternating direction method of multipliers (ADMMs), where the Bregman iteration is adopted for solving the subproblem with morphology regularization. Both simulation experiments and tests on real data show that the proposed method can suppress the outliers and guarantee the detection rate, leading to excellent correctness and completeness.