The elevation image quality of tomographic synthetic aperture radar (TomoSAR) data depends mainly on the elevation aperture size, number of baselines, and baseline distribution. In TomoSAR, due to the restricted number of baselines with irregular distributions, the elevation imaging quality is always unacceptable using the conventional spectral analysis approach. Therefore, for a given limited number of irregular baselines, the completion of data for the unobserved virtual uniform baseline distribution should be addressed to improve the spectral analysis-based TomoSAR reconstruction quality. We propose an regularization-based unobserved baselines’ data estimation method for TomoSAR, which uses the geometric imaging relationship between the observed and unobserved baseline distributions. In the proposed method, we first estimate the transformation matrix between the acquisitions and the data of virtual uniform baseline distribution by solving an optimization problem, before calculating the data for virtual baseline distribution based on the acquisitions and the transformation matrix. Finally, the elevation reflectivity function is recovered using the spectral analysis method based on the estimated data. Compared with the reconstructed results only based on the limited irregular acquisitions, the image recovered using the dataset with a virtual uniform baseline distribution can improve the elevation image quality in an efficient manner.