A Joint Markov Random Field Approach for SAR Interferogram Filtering and Unwrapping

Abdallah, Wajih; Abdelfattah, Riadh

doi:10.1109/jstars.2016.2540519

Cited by 9 publications

(5 citation statements)

References 40 publications

(82 reference statements)

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“…Various cost criteria f based on coherence and phase gradients have been discussed at length in the literature [23,24]. For efficiency, we choose the average coherence of three nodes in the edge as the cost element [25].…”

Section: B Lp Mathematical Framework In Modified Spumentioning

confidence: 99%

An Improved Method of the Finite-Difference Sparse Phase Unwrapping

Shan

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Phase unwrapping (PU) is a significant problem for reconstructing the deformation field during Synthetic Aperture Radar Interferometry (InSAR) analysis. The various twodimensional (2-D) PU algorithms can be divided into two categories: (1) path-following methods and (2) optimization based methods. The former predefine an integration path in which the phase gradient is integrated to obtain the unwrapped results. The latter are path-independent and error criterion oriented. The integration of finite differences and the Minimum Cost Flow (MCF) solver describes a global optimization problem between phase residues over closed spatial triangles computed over redundant neighboring edge sets. We propose a modified network using a simplified mathematical formulation for linear programming (LP) in finite differences PU. Our algorithm has three major advantages over current methods. (1) The modified network combines Delaunay triangulation and K nearest points to avoid isolated regions in the PU process. (2) Modified formulation of the LP solver can directly obtain the phase ambiguity cycles of all points without integration. (3) The combination of the new network and modified LP can achieve better PU results than other state-of-the-art techniques. We applied our method to synthetic and real data from the 24, Jan 2020 Mw 6.7 earthquake in Doğanyol-Sivrice, Turkey and the 8, Aug 2017 Mw 6.5 earthquake in Jiuzhaigou, China. Comprehensive comparisons validate the effectiveness of our method. Index Terms-finite differences, linear programming, minimum cost flow (MCF), phase unwrapping, SAR interferometry (InSAR).

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Section: B Lp Mathematical Framework In Modified Spumentioning

confidence: 99%

An Improved Method of the Finite-Difference Sparse Phase Unwrapping

Shan

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…While ICM is relatively faster than simulated annealing, it can lock at a local minimum. In recent years, some studies have suggested the use of a genetic algorithm to minimize an energy function used for phase unwrapping problem, this is able to converge to a global minimum at a reasonable time [17,18]. In this article a genetic algorithm is used to minimize the cost function f (x) detailed below (Eq.…”

Section: Cost Functionmentioning

confidence: 99%

“…This function is minimized by a genetic algorithm. This algorithm is capable of converg-ing to a global minimum at a reasonable time [17,18].…”

Section: Introductionmentioning

confidence: 99%

New Interferometric Phase Unwrapping Method Based on Energy Minimization From Contextual Modeling

Tlili

Cavayas

Foucher

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…However, its performance will be undermined when there are not enough homogeneous pixels in the neighborhood or heterogeneous pixels are included. After that, a complex Markov random field (CMRF) filter, which estimates the phase by minimizing the local energy function in the window, was proposed [11], [12]. To adapt to the changes of fringe pattern, Li et al proposed a variable window CMRF filter [13].…”

Section: Introductionmentioning

confidence: 99%

A Novel Adaptive InSAR Phase Filtering Method Based on Complexity Factors

Xu,

Wang,

et al. 2023

Chinese J. Elect.

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Phase filtering is an essential step in interferometric synthetic aperture radar (InSAR) imaging. For interferograms of complicated and changeable terrain, the increasing resolution of InSAR images makes it even more difficult. In this paper, a novel adaptive InSAR phase filtering method based on complexity factors is proposed. Firstly, three complexity factors based on the noise distribution and terrain slope information of the interferogram are selected. The complexity indicator composed of three complexity factors is used to guide the adaptive selection of the most suitable and effective filtering strategies for different areas. Then, the complexity scalar is calculated, which can guide the adaptive local fringe frequency estimation and adaptive parameters calculation in different filter methods. Finally, validations are performed on the simulated and real data. The performance comparison between the other three representative phase filtering method and the proposed method have validated the effectiveness and superiority of the proposed method.

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A Joint Markov Random Field Approach for SAR Interferogram Filtering and Unwrapping

Cited by 9 publications

References 40 publications

An Improved Method of the Finite-Difference Sparse Phase Unwrapping

An Improved Method of the Finite-Difference Sparse Phase Unwrapping

New Interferometric Phase Unwrapping Method Based on Energy Minimization From Contextual Modeling

A Novel Adaptive InSAR Phase Filtering Method Based on Complexity Factors

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