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.

Circular SAR Incoherent 3D Imaging with a NeRF-Inspired Method

“…Under this assumption, we establish a linear mapping relationship between the multiaspect SAR amplitude images and a real-valued 4D (spatial location (x, y, z) and radar azimuth angle θ) scattered field of the target. The image formation model deduced in this section is inspired by those used in incoherent tomography techniques [32] and multimaster SAR tomography techniques [33,34], while the neural network in the next section is used to solve the inverse problem of this image formation model.…”

P-Band UAV-SAR 4D Imaging: A Multi-Master Differential SAR Tomography Approach

“…As discussed earlier, the SM signal model with a fixed master image is limited in long-baseline cases. Previous research [39,40,42,43] has proposed a bistatic-like MM signal model for atmospheric phase error elimination, which can be adopted for image pairing in D-TomoSAR. This model allows for an unfixed master image.…”

“…To address this issue, we propose a solution through the idea of MM D-TomoSAR using a non-fixed master image. Although the bistatic-like MM model has been introduced previously in [39,40,42,43], it is a limited one, and only bistatic-like SAR images can be paired to generate interferograms to avoid atmospheric phase errors. In contrast, the proposed model effectively utilizes the advantage that UAV-SAR is free from atmospheric phase errors.…”

SAR Tomography at the Limit: Building Height Reconstruction Using Only 3–5 TanDEM-X Bistatic Interferograms

“…Spectral estimation based conventional TomoSAR inversion algorithms, however, require complex spectral samples at several wavenumbers, phase-normalized to a single master phase. In a current parallel work by one of the authors [28] it is shown that pixel-wise TomoSAR using multi-master acquisitions is a non-convex hard to solve problem. This is true for pixel-wise tomographic inversion or for point scatterers.…”