LAUSR

In recent years, the synthetic aperture radar tomography (TomoSAR) technique is becoming an increasingly hot research topic in the 3-D reconstruction of the structure of urban buildings. The robustness of the many advanced nonparametric spectral estimation methods in use is based on a high number of observations. This letter aims to evaluate the ability of the five nonparametric estimator techniques, including propagator, generalized maximum entropy (GME), minimum norm (MN), singular value decomposition (SVD), and Capon in the separation of scattering contributions with a small number of SAR images. The performance comparison of the employed algorithms is carried out on the simulated and real datasets. The study results show that the novel high-resolution propagator and GME estimators, respectively, based on the partitioning and extrapolation of the covariance matrix, can discriminate the number of scatterers in one azimuth-range resolution and satisfactorily prevent the appearance of undesirable sidelobes. The experimental results on real SAR images acquired by the TerraSAR-X satellite show the proposed propagator spectral estimation can significantly improve the 3-D building reconstruction in the urban environment. Also, the estimated height of the scatters using the propagator technique is similar to the data obtained from ground observations.