Numerical simulation of tomographic-SAR imaging and object reconstruction using compressive sensing with L_(1/2)-norm regularization

By making use of multiple acquisitions of synthetic aperture radar(SAR) observations over the same area, tomographic-SAR(tomo-SAR) technology can achieve three-dimensional(3-D) imaging of the objects of interest. The compressive sensing(CS) approach has been applied to deal with the sparseness of the elevation signals.Due to its sparsity and convexity, the L1-norm regularization, as an approximated L0-norm with an exact solution,has been employed in CS to reconstruct the reflectivity profile of the objects. In this paper, based on our studies on polarimetric scattering and SAR imaging simulations, we produce numerical multi-pass tomo-SAR observations of the terrain object. Then, we present the CS with novel L1/2-norm regularization to realize 3-D reconstruction. As a non-convex optimization problem, the L1/2-norm regularization is solved by an iterative algorithm. This numerical simulation of tomo-SAR imaging and 3-D reconstruction of the object modeling can be of great help for parameterized analysis of tomo-SAR imagery. As an example, a tomo-SAR image and 3-D reconstruction of the Beijing National Stadium model are presented.

By making use of multiple acquisitions of synthetic aperture radar （SAR） observations over the same area, tomographic-SAR （tomo-SAR） technology can achieve three-dimensional （3-D） imaging of the objects of interest. The compressive sensing （CS） approach has been applied to deal with the sparseness of the elevation signals. Due to its sparsity and convexity, the L1-norm regulariza- tion, as an approximated Lo-norm with an exact solution, has been employed in CS to reconstruct the reflectivity profile of the objects. In this paper, based on our studies on polarimetric scattering and SAR imaging simulations, we produce numerical multi-pass tomo-SAR observations of the terrain object. Then, we present the CS with novel L1/2- norm regularization to realize 3-D reconstruction. As a non-convex optimization problem, the L1/2-norm regularization is solved by an iterative algorithm. This numerical simulation of tomo-SAR imaging and 3-D reconstruction of the object modeling can be of great help for parameterized analysis of tomo-SAR imagery. As an example, a tomo-SAR image and 3-D reconstruction of the Beijing National Stadium model are presented.