Building profile reconstruction based on double subspace TomoSAR

Synthetic aperture radar (SAR) tomography (TomoSAR) is an attractive technology for the three-dimensional (3D) reconstruction of buildings through multiple coherent SAR images. This technique can overcome the limitations of interferometry in multi-scatterer resolution and play a significant role in the analysis of urban areas. The robustness of the many advanced nonparametric spectral estimation approaches is founded on a high number of observations. In this work, we evaluate the abilities of five nonparametric spectral estimation methods, including double subspace (DS), maximum entropy (ME), minimum norm (MN), Capon, and beamforming (BF), to separate scattering contributions with a small number of SAR images in the tomographic reconstruction of urban environments. DS is a high-resolution estimator based on the existence of non-uniform noise, which assesses the noise subspace via eigendecomposition of some appropriately designed matrix without comprehending the noise covariance matrix. The study results indicate that DS can distinguish the scatterers in one azimuth-range resolution and satisfactorily dissuade the appearance of unpleasant side lobes. To investigate the effectiveness of DS and compare that with other expressed methods, both simulated and real SAR datasets were acquired from TerraSAR-X strip-map images of the city of Tehran, Iran. The experimental results on real SAR images demonstrate that the DS can remarkably enhance building reconstruction in the urban environment. The estimated height of the scatters utilizing the DS technique is similar to the data obtained from ground observations.