Single-Look Multi-Master SAR Tomography: An Introduction

TL;DR

This paper introduces a new framework for single-look multi-master SAR tomography, proposing algorithms for sparse recovery, and demonstrates that multi-master approaches can outperform single-master methods in layover separation.

Contribution

It develops a generic inversion framework with two novel algorithms for single-look multi-master SAR tomography, including theoretical analysis and validation on real data.

Findings

Multi-master approach can outperform single-master in layover separation.

Proposed algorithms are effective for real TerraSAR-X data.

Theoretical solutions for NLS subproblem are derived.

Abstract

This paper 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 single and double scatterers, and propose a generic inversion framework. The core of this framework is 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 one-dimensional 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 height ground truth made available via a SAR imaging geodesy and simulation framework. This shows empirically that the \emph{single-master} approach, if applied to a single-look \emph{multi-master} stack, can be insufficient for layover separation, and the \emph{multi-master} approach can indeed perform slightly better (despite being computationally more expensive) even in the case of single scatterers. Besides, this paper also sheds light on the special case of single-look bistatic SAR tomography, which is relevant for current and future SAR missions such as TanDEM-X and Tandem-L.