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Single-Look Multi-Master SAR Tomography An Introduction

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Document pages: 23 pages

Abstract: This paper addresses the general problem of single-look multi-master SARtomography. For this purpose, we establish the single-look multi-master datamodel, analyze its implications for single and double scatterers, and propose ageneric inversion framework. The core of this framework is nonconvex sparserecovery, for which we develop two algorithms: one extends the conventionalnonlinear least squares (NLS) to the single-look multi-master data model, andthe other is based on bi-convex relaxation and alternating minimization(BiCRAM). We provide two theorems for the objective function of the NLSsubproblem, which lead to its analytic solution up to a constant phase angle inthe one-dimensional case. We also report our findings from the experiments ondifferent acceleration techniques for BiCRAM. The proposed algorithms areapplied to a real TerraSAR-X data set, and validated with height ground truthmade available via a SAR imaging geodesy and simulation framework. This showsempirically 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 beingcomputationally more expensive) even in the case of single scatterers. Besides,this paper also sheds light on the special case of single-look bistatic SARtomography, which is relevant for current and future SAR missions such asTanDEM-X and Tandem-L.

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