Realistic Lower Bound on Elevation Estimation for Tomographic SAR

Yang, Bo‐Suk; Xu, Huaping; Liu, Wei; You, Yancheng; Xie, Xiaozhen

doi:10.1109/jstars.2018.2834950

Cited by 10 publications

(9 citation statements)

References 46 publications

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“…The mathematical representation of online CP decomposition model is shown as Equation ( 10), and it is different from the standard CP decomposition model shown as Equation (9). The online CP decomposition deal with the slices in tensor to extract the low-rank vectors instead of processing the overall tensor.…”

Section: Olcp-insar For Tensor Modelmentioning

confidence: 99%

“…A filtered interferogram is regarded as the sum of factor matrices shown as (9). If factor matrices contain a lot of redundant information, it implies the current CP rank is higher than the suitable value.…”

Section: Cp Rank Estimation Via Multilinear Pcamentioning

confidence: 99%

“…The localization of these reference points depends on the latitude and longitude information provided by standard SAR image products. If InSAR stack data has a layover effect caused by some tall buildings, tomographic interferometry is introduced to extract the scatter points and invert them to the ortho-coordinate [9]. It can be seen that the accuracy of interferomeric phases affects the inversion of 3D surface information.…”

Section: Introductionmentioning

confidence: 99%

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An Optimized Filtering Method of Massive Interferometric SAR Data for Urban Areas by Online Tensor Decomposition

2020

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The filtering of multi-pass synthetic aperture radar interferometry (InSAR) stack data is a necessary preprocessing step utilized to improve the accuracy of the object-based three-dimensional information inversion in urban area. InSAR stack data is composed of multi-temporal homogeneous data, which is regarded as a third-order tensor. The InSAR tensor can be filtered by data fusion, i.e., tensor decomposition, and these filters keep balance in the noise elimination and the fringe details preservation, especially with abrupt fringe change, e.g., the edge of urban structures. However, tensor decomposition based on batch processing cannot deal with few newly acquired interferograms filtering directly. The filtering of dynamic InSAR tensor is the inevitable challenge when processing InSAR stack data, where dynamic InSAR tensor denotes the size of InSAR tensor increases continuously due to the acquisition of new interferograms. Therefore, based on the online CANDECAMP/PARAFAC (CP) decomposition, we propose an online filter to fuse data and process the dynamic InSAR tensor, named OLCP-InSAR, which performs well especially for the urban area. In this method, CP rank is utilized to measure the tensor sparsity, which can maintain the structural features of the InSAR tensor. Additionally, CP rank estimation is applied as an important step to improve the robustness of Online CP decomposition - InSAR(OLCP-InSAR). Importing CP rank and outlier’s position as prior information, the filter fuses the noisy interferograms and decomposes the InSAR tensor to acquire the low rank information, i.e., filtered result. Moreover, this method can not only operate on tensor model, but also efficiently filter the new acquired interferogram as matrix model with the assistance of chosen low rank information. Compared with other tensor-based filters, e.g., high order robust principal component analysis (HoRPCA) and Kronecker-basis-representation multi-pass SAR interferometry (KBR-InSAR), and the widespread traditional filters operating on a single interferometric pair, e.g., Goldstein, non-local synthetic aperture radar (NL-SAR), non-local InSAR (NL-InSAR), and InSAR nonlocal block-matching 3-D (InSAR-BM3D), the effectiveness and robustness of OLCP-InSAR are proved in simulated and real InSAR stack data. Especially, OLCP-InSAR can maintain the fringe details at the regular building top with high noise intensity and high outlier ratio.

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Section: Olcp-insar For Tensor Modelmentioning

confidence: 99%

Section: Cp Rank Estimation Via Multilinear Pcamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An Optimized Filtering Method of Massive Interferometric SAR Data for Urban Areas by Online Tensor Decomposition

2020

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“…Finally, 20 passes real data acquired by the sensors of TerraSAR-X/Tandem-X, over 2014, are utilized for demonstration. Detailed information about the real data can be found in [31]. One of the SAR intensity images is shown in Fig.…”

Section: Real Datamentioning

confidence: 99%

A novel statistical model for differential synthetic aperture radar tomography

Yang¹,

Xu²,

Liu³

et al. 2018

Meas. Sci. Technol.

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Deterministic differential Tomographic SAR (D-TomoSAR) model, based on geometrical derivations and the assumption of accurate phase calibration, is widely employed for spatially locating and temporally monitoring the point-like scatterers in the past. In this work, we model phase miscalibration effects of the extended scatters caused by partial correlation, i.e., the decorrelation effects from temporal and spatial changes as well as the residual atmospheric and deformation effect after preprocessing. Starting from the origin of 4-D SAR focusing, correlation of target is analysed, and a statistical D-TomoSAR model accounting for partial correlation effects is proposed. Based on the proposed model, a simulator for D-TomoSAR stack is designed using the Cholesky decomposition. Moreover, a linear minimum mean square error (LMMSE) estimator based on the proposed model is developed for height and deformation velocity estimation of extended scatterer. Reconstruction results with both simulated data and real data acquired by TerraSAR-X/Tandem-X sensors are provided to demonstrate the effectiveness of the proposed model.

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“…[8], an iterative reweighted alternating-direction method has been proposed for the SAR-tomography imaging, which reduces computation time notably. A correlated SAR-tomography model has been introduced in the work [9]. It enables one to study the effects of random residual phase and noise.…”

Section: Introductionmentioning

confidence: 99%

Bayesian compressive sensing for synthetic-aperture radar tomography imaging

Ren¹,

Qin²,

Qiao³

2020

Ukr. J. Phys. Opt.

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To achieve high-resolution three-dimensional images, a number of imaging methods based on compressive sensing (CS) have been suggested in the recent years for synthetic-aperture radar (SAR) tomography. However, the CS-based methods are sensitive to noise. In this work, we develop a new Bayesian compressive sensing (BCS) imaging method for the SAR tomography. In the framework of BCS, a 'sparseness' prior distribution of the imaging scene and an additive noise are properly considered in the imaging process. As a consequence, the BCS-based method under the conditions of low noise levels can provide a better performance than the common norm-based CS methods. The results obtained via simulations of our SAR-tomography imaging method confirm its advantages.

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Realistic Lower Bound on Elevation Estimation for Tomographic SAR

Cited by 10 publications

References 46 publications

An Optimized Filtering Method of Massive Interferometric SAR Data for Urban Areas by Online Tensor Decomposition

An Optimized Filtering Method of Massive Interferometric SAR Data for Urban Areas by Online Tensor Decomposition

A novel statistical model for differential synthetic aperture radar tomography

Bayesian compressive sensing for synthetic-aperture radar tomography imaging

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