Comparative Experiments on Separation Performance of Overlapping Scatterers with Several Tomography Imaging Methods

Abstract

The tomographic technique has attracted much attention because of its ability to separate overlapping scatterers in urban Synthetic Aperture Radar (SAR) images. The general method of SAR Tomography (TomSAR) imaging combines the following two aspects: estimating the distribution of the scatterers in the elevation direction and determining the number of strong scatterers in an overlapped pixel. This study applied several sophisticated spectrum estimations (e.g., Orthogonal Matching Pursuit, Sparse Learning via Iterative Minimization and Multiple Signal Classification) and model order selection approaches (e.g., Bayesian information criterion and generalized likelihood ratio test) with highly technical potential to recover the simulated overlapping scatterers. This simulation experiment is based on the parameters of the AIRCAS X-band TomoSAR data from Emei, Sichuan, China. The Cramér-Rao Lower Bound (CRLB) and recovery probability are used to evaluate the performances of different methods for the separation of overlapped scatterers. The experimental results revealed the following: (1) the standard deviation of estimation using second-order statistics is smaller than that of a single observation vector, especially when the number of acquisitions is very limited; (2) the amplitude ratio, phase difference, and elevation spacing between overlapping scatterers will have a significant impact on the different kinds of algorithms; and (3) the phase difference between overlapping scatterers will make the phase center estimation of greedy algorithm or spectrum estimation algorithm biased.

Keywords

• array interferometric sar
• tomosar imaging
• separation of overlapped scatterers
• cramér-rao lower bound (crlb)
• recovery probability