Multi‐baseline InSAR phase unwrapping method based on mixed‐integer optimisation model

Jin, Biao; Guo, Jun; Wei, Pengliang; Su, Baofeng; He, Dongjian

doi:10.1049/iet-rsn.2017.0543

Cited by 9 publications

(8 citation statements)

References 24 publications

(23 reference statements)

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“…))/2π are not always integers, so rounding operation is used to get the estimations∆ x k r (i , j ) and∆ y k r (i , j ) in Equations of ( 17) and (18).…”

Section: Lpm-tspa Methodsmentioning

confidence: 99%

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Refined Two-Stage Programming-Based Multi-Baseline Phase Unwrapping Approach Using Local Plane Model

Lan

Xing

2019

Remote Sensing

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The problem of phase unwrapping (PU) in synthetic aperture radar (SAR) interferometry (InSAR) is caused by the measured range differences being ambiguous with the wavelength. Therefore, multi-baseline (MB) is a key processing step of MB InSAR. Compared with the traditional single-baseline (SB) PU, MB PU is advantageous in solving steep terrain due to its ability to break through the constraint of the phase continuity assumption. However, the accuracy of most of the existing MB PU methods is still limited to its mathematical foundation, i.e., the Chinese remainder theorem (CRT) is too sensitive to measurement bias. To solve this issue, this paper presents a refined algorithm based on the two-stage programming MB PU approach (TSPA) proposed by H. Yu. The significant advantage of the refined TSPA method (abbreviated as LPM-TSPA) is that it improves the performance of stage 1 of TSPA through assuming terrain height surface in the neighborhood pixels can be approximated by a plane to combine more information of the interferometric phase in the local region to estimate the ambiguity number gradient. The experiment results indicate that the LPM-TSPA method can significantly improve the accuracy of the MB PU solution.

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“…))/2π are not always integers, so rounding operation is used to get the estimations∆ x k r (i , j ) and∆ y k r (i , j ) in Equations of ( 17) and (18).…”

Section: Lpm-tspa Methodsmentioning

confidence: 99%

“…To improve the robustness to noise, Ref. [18] put forward a MB PU method based on the mix-integer optimization model. Ref.…”

Section: Introductionmentioning

confidence: 99%

Refined Two-Stage Programming-Based Multi-Baseline Phase Unwrapping Approach Using Local Plane Model

Lan

Xing

2019

Remote Sensing

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“…[14] proposed the L∞-norm programming criterion applied to the MB PU. To improve the robustness to noise, [15] presented a closed-form robust CRT method, and [16] put forward a MB PU method based on the mix-integer optimization model. More than that, [17] proposed a Kalman filtering-based MB PU method, and a wavelet approach-based MB PU method was presented in [18].…”

Section: Introductionmentioning

confidence: 99%

Extended Phase Unwrapping Max-Flow/Min-Cut Algorithm for Multibaseline SAR Interferograms Using a Two-Stage Programming Approach

Zhou

Lan

Xia

et al. 2020

Sensors

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Multi-baseline (MB) phase unwrapping (PU) is a key step of MB synthetic aperture radar (SAR) interferometry (InSAR). Compared with the traditional single-baseline (SB) PU, MB PU is applicable to the area where topography varies violently without obeying the phase continuity assumption. A two-stage programming MB PU approach (TSPA) proposed by H. Yu. builds the link between SB and MB PUs, so many existing classical SB PU methods can be transplanted into the MB domain. In this paper, an extended PU max-flow/min-cut (PUMA) algorithm for MB InSAR using the TSPA, referred to as TSPA-PUMA, is proposed, consisting of a two-stage programming procedure. In stage 1, phase gradients are estimated based on Chinese remainder theorem (CRT). In stage 2, a Markov random field (MRF) model of PUMA is designed for modeling local contextual dependence based on the phase gradients obtained by stage 1. Subsequently, the energy of the MRF model is minimized by graph cuts techniques. The experiment results illustrate that the TSPA-PUMA method can drastically enhance the accuracy of the original PUMA method in the rugged area, and is more efficient than the original TSPA method. In addition, the noise robustness of TSPA-PUMA can be improved through adding more interferograms with different baseline lengths.

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“…Thompson [13] proposed a recursive MBPU method that analyzes the relationship between the long-baseline and the short-baseline interferograms, and then uses the short-baseline to obtain the PU results of the long-baseline. B. Jin [23] proposed an MB InSAR PU method based on a mixed integer optimization model. H. Yu [12] proposed a groundbreaking MBPU framework that can introduce an SBPU model to an MBPU method, which has important implications for MBPU research.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Filtering Multi-Baseline Phase Unwrapping Method Based on a Two-Stage Programming Approach

Gao

Tang

et al. 2020

Applied Sciences

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Phase unwrapping (PU) has been a key step in the processing of interferometric synthetic aperture radar (InSAR) data, and its processing accuracy will directly affect the reconstruction results of digital elevation models (DEMs). The traditional single-baseline (SB) PU must be calculated under continuity assumptions. However, multi-baseline (MB) PU can get rid of the limitation of continuity assumption, so reasonable results can be obtained in regions with large gradient changes. However, the poor noise robustness of MBPU has always been a key problem. To address this issue, we transplant three Bayesian filtering methods with a two-stage programming approach (TSPA), and propose corresponding MBPU models. First, we propose a gradient-estimation method based on the first step of TSPA, and then the corresponding PU model is determined according to different Bayesian filtering. Finally, the wrapped phase can be obtained by unwrapping, one by one, using an effective quality map based on heapsort. These methods can improve the robustness of the MBPU methods. More significantly, this paper establishes a novel TSPA-based Bayesian filtering MBPU framework for the first time. This is of great significance for broadening the research of MBPU. The proposed methods experiments on simulated and real MB InSAR datasets. From the results, we can see that the TSPA-based Bayesian filtering MBPU framework can significantly improve the robustness of the MBPU method.

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Multi‐baseline InSAR phase unwrapping method based on mixed‐integer optimisation model

Cited by 9 publications

References 24 publications

Refined Two-Stage Programming-Based Multi-Baseline Phase Unwrapping Approach Using Local Plane Model

Refined Two-Stage Programming-Based Multi-Baseline Phase Unwrapping Approach Using Local Plane Model

Extended Phase Unwrapping Max-Flow/Min-Cut Algorithm for Multibaseline SAR Interferograms Using a Two-Stage Programming Approach

Bayesian Filtering Multi-Baseline Phase Unwrapping Method Based on a Two-Stage Programming Approach

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