Comparison of multiple scatterer detection methods in differential SAR tomography

Abstract

Synthetic aperture radar (SAR) tomography is a technique that offers new perspectives in the 4-D reconstruction (3-D spatial information plus deformation information), particularly, of urban areas. SAR tomography allows resolving layovers by handle the situation where multiple scatterers exist within a resolution cell. The detection of scatterers is however a critical and challenging issue. Single scatterers can be simply detected by the amplitude dispersion method, but it does not meet the application needs of urban buildings. In this paper, we study a variety of methods for scatterer detection in differential SAR Tomography. We mainly investigate three detection approaches based on signal estimation and detection: sequential generalized likelihood ratio test (SGLRT), sup-GLRT, and fast-sup-GLRT. Firstly, theoretical performances of each method are analyzed in details. Secondly, experimental validation is carried out through simulation data and real data from TerraSAR-X satellite corresponding to a building—North Star Times Tower in Beijing, China. Finally, we compared the signal detecting based methods involved in this paper, with the compressed sensing (CS) methods (e.g., regularized orthogonal matching pursuit (ROMP) and L1-norm regularization). The results demonstrate that the CS methods achieve only the spatial information of the scatterers, whereas the signal detecting based methods are capable of obtaining the backward scattering power information of each scatterer in parallel with the spatial information. This leads to the conclusion that the signal detecting based methods gain more information about the scatterer than the CS-based methods. Furthermore, the detection accuracy and time cost of the aforementioned methods are presented and discussed.