Single-Look Multi-Master SAR Tomography: An Introduction

Abstract

This article addresses the general problem of single-look multi-master SAR tomography. For this purpose, we establish the single-look multi-master data model, analyze its implications for the single and double scatterers, and propose a generic inversion framework. The core of this framework is the nonconvex sparse recovery, for which we develop two algorithms: one extends the conventional nonlinear least squares (NLS) to the single-look multi-master data model and the other is based on bi-convex relaxation and alternating minimization (BiCRAM). We provide two theorems for the objective function of the NLS subproblem, which lead to its analytic solution up to a constant phase angle in the 1-D case. We also report our findings from the experiments on different acceleration techniques for BiCRAM. The proposed algorithms are applied to a real TerraSAR-X data set and validated with the height ground truth made available by an SAR imaging geodesy and simulation framework. This shows empirically that the single-master approach, if applied to a single-look multi-master stack, can be insufficient for layover separation, and the multi-master approach can indeed perform slightly better (despite being computationally more expensive) even in the case of single scatterers. In addition, this article also sheds light on the special case of single-look bistatic SAR tomography, which is relevant for the current and future SAR missions such as TanDEM-X and Tandem-L.