Bistatic-Like Differential SAR Tomography

Gao, Nan; Zhu, Xiao Xiang

doi:10.1109/tgrs.2019.2902814

Cited by 13 publications

(9 citation statements)

References 38 publications

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“…By selecting the PS point, the low coherence region can be avoided and the spatial and temporal decoherence problems can be solved effectively. This experiment mainly adopts the amplitude dispersion index method for PS point selection, which mainly uses the statistical distribution of amplitude to select stable PS points (Ge & Zhu, 2019a)，The PS points are selected by analyzing the time series composed of echo amplitudes. The main rule is to select the points with larger MSR values, where 𝑀𝑆𝑅 = 𝜇 𝜎 ,𝜇 and 𝜎 are the mean and standard deviation of the amplitude of each image element of N SAR images respectively, and the amplitude departure index method is simple to calculate, suitable for processing multi-scene image data and for processing large blocks of data in blocks, as shown in Figure 5 for the PS points of this experimental selection area.…”

Section: Interference Processing and Ps Point Selectionmentioning

confidence: 99%

Differential Sar Tomography of Large-Scale Water Conservancy Projects Under Steep Terrain – The Case Study of Laxiwa Hydropower Station

Li,

Pang,

2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Differential SAR Tomography (D-TomoSAR), as an extension of InSAR technology, combines D-InSAR and TomoSAR technology to achieve imaging in the height-deformation rate (s-v) plane. It not only solves the layover problem of SAR imaging, but also obtains the height and deformation rate of each scatterer within the image element. The technique is currently mainly applied to complex scenes in urban areas where the layover problem is serious, and the layover effect also exists for hydropower plants with extremely steep slopes. In this paper, the Differential SAR Tomography technique is applied to the four-dimensional imaging of hydraulic engineering for the first time, taking the La Siwa hydropower station as an example. This experiment establishes the signal model based on permanent scatterer points, so interferometric processing and PS point selection should be performed to obtain the differential interferogram sequence and PS points in the region; the orthogonal matching pursuit (OMP) algorithm is selected for differential SAR tomography imaging processing to reconstruct the elevation-deformation rate backward scattering profile. The experiments use the 23-view TerraSAR-X satellite one-meter resolution time series image dataset for deformation monitoring of the Laxiwa hydropower dam in GuiDe County, Qinghai Province, China, and finally the resolution and reconstruction estimation performance are evaluated by theoretical analysis and application case study. A comparison with the traditional InSAR technique shows that the differential SAR tomography technique not only maintains the advantages of high resolution, but also significantly improves the probability of accurate reconstruction of scattered points, and achieves higher accuracy in estimating the deformation of hydropower dams. This paper mainly discusses the application of differential SAR tomography technology in water conservancy projects, hoping to provide reference and help for the future large-scale application of differential SAR tomography technology in hydroelectric dam deformation monitoring.

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Section: Interference Processing and Ps Point Selectionmentioning

confidence: 99%

Differential Sar Tomography of Large-Scale Water Conservancy Projects Under Steep Terrain – The Case Study of Laxiwa Hydropower Station

Li,

Pang,

2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…A sinusoidal basis function was used for modeling periodical motion induced by temperature change. The vertical Rayleigh resolution at the scene center is approximately 12.66 m. The Crámer-Rao lower bound (CRLB) of height estimates given the aforementioned periodical deformation model [25] and a nominal signal-to-noise ratio (SNR) of 2 dB is approximately 1.10 m. NLS and L1RLS were applied to this stack for tomographic reconstruction. The latter was solved by Algorithm 4 augmented with diagonal preconditioning and over-relaxation (see Section IV-B), where we set β = 1.8 and the choice of α is irrelevant (since A is a Fourier matrix).…”

Section: (Bottom)mentioning

confidence: 99%

“…Zhu and Bamler [24] extended the Tikhonov regularization, NLS, and compressive-sensing approaches to a mixed TerraSAR-X and TanDEM-X stack by using the preestimated covariance matrix. Ge and Zhu [25] proposed a framework for SAR tomography using only the bistatic or pursuit monostatic acquisitions: nondifferential SAR tomography for height estimation by using the bistatic or pursuit monostatic interferograms and differential SAR tomography for deformation estimation by using conventional repeat-pass interferograms and the previous height estimates as the deterministic prior [25]. However, the single-look single-master data model still underlies the algorithms in both publications.…”

Section: Introductionmentioning

confidence: 99%

“…While both previously mentioned categories have been intensively studied, it is not the case for single-look multi-master SAR tomography. To the best of our knowledge, all the existing work to date toward single-look multi-master SAR tomography is still incorrectly based on the single-look single-master data model [24], [25]. As will be demonstrated later with a real SAR data set, this approach can be insufficient for layover separation, even if the elevation distance between two scatterers is significantly larger than the Rayleigh resolution.…”

Section: Introductionmentioning

confidence: 99%

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Single-Look Multi-Master SAR Tomography: An Introduction

Gao

Bamler

Hong

et al. 2021

IEEE Trans. Geosci. Remote Sensing

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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.

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“…The essence of D-TomoSAR is two-dimensional (2D) (spatial and temporal) sparse spectrum estimation [6,7]. Specifically, the distribution of the spatial-temporal baselines determines the sampling form of the 2D spectrum [39,40].…”

mentioning

confidence: 99%

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

et al. 2023

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Due to its rapid deployment, high-flexibility, and high-accuracy advantages, the unmanned-aerial-vehicle (UAV)-based differential synthetic aperture radar (SAR) tomography (D-TomoSAR) technique presents an attractive approach for urban risk monitoring. With its sufficiently long spatial and temporal baselines, it offers elevation and velocity resolution beyond the dimensions of range and azimuth, enabling four-dimensional (4D) SAR imaging. In the case of P-band UAV-SAR, a long spatial-temporal baseline is necessary to achieve high enough elevation-velocity dimensional resolution. Although P-band UAV-SAR maintains temporal coherence, it still faces two issues due to the extended spatial baseline, i.e., low spatial coherence and high sidelobes. To tackle these problems, we introduce a multi-master (MM) D-TomoSAR approach, contributing three main points. Firstly, the traditional D-TomoSAR signal model is extended to a MM one, which improves the average coherence coefficient and the number of baselines (NOB) as well as suppresses sidelobes. Secondly, a baseline distribution optimization processing is proposed to equalize the spatial–temporal baseline distribution, achieve more uniform spectrum samplings, and reduce sidelobes. Thirdly, a clustering-based outlier elimination method is employed to ensure 4D imaging quality. The proposed method is effectively validated through computer simulation and P-band UAV-SAR experiment.

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Bistatic-Like Differential SAR Tomography

Cited by 13 publications

References 38 publications

Differential Sar Tomography of Large-Scale Water Conservancy Projects Under Steep Terrain – The Case Study of Laxiwa Hydropower Station

Differential Sar Tomography of Large-Scale Water Conservancy Projects Under Steep Terrain – The Case Study of Laxiwa Hydropower Station

Single-Look Multi-Master SAR Tomography: An Introduction

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

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