Multichannel Phase Unwrapping With Graph Cuts

Ferraioli, Giampaolo; Shabou, Aymen; Tupin, Florence; Pascazio, Vito

doi:10.1109/lgrs.2009.2021165

Cited by 60 publications

(30 citation statements)

References 16 publications

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“…The first one is a weighted maximum likelihood estimator that exploits the weighted log-likelihood terms D i , without any regularization; the second one is a Maximum a Posteriori that implements the estimator of Eq. (15) with a sub-optimal minimization procedure based on Iterated Conditional Modes (ICM)); finally the MCPU proposed in [23] implements a Maximum a Posteriori estimator based on the statistical independence between interferograms and on the use of a graph-cut-based optimal minimization procedure.…”

Section: Validation On Numerical Simulationsmentioning

confidence: 99%

“…These methods propose to exploit the statistical distribution of the acquired data and to implement instruments provided by both classical [19], [20] and Bayesian estimation theory. In particular, for the latter when Markov Random Fields (MRF) theory is used for modeling the unknown height profile the so-called Bayesian Markovian estimation framework arises [21], providing very effective results in the multi-channel case [22], [23]. Interesting previous works proposed to apply the Bayesian Markovian framework to single-channel interferograms [24], [3].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Parisar: Patch-Based Estimation and Regularized Inversion for Multibaseline SAR Interferometry

Ferraioli

Deledalle

Denis

et al. 2018

IEEE Trans. Geosci. Remote Sensing

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Abstract-Reconstruction of elevation maps from a collection of SAR images obtained in interferometric configuration is a challenging task. Reconstruction methods must overcome two difficulties: the strong interferometric noise that contaminates the data, and the 2π phase ambiguities. Interferometric noise requires some form of smoothing among pixels of identical height. Phase ambiguities can be solved, up to a point, by combining linkage to the neighbors and a global optimization strategy to prevent from being trapped in local minima. This paper introduces a reconstruction method, PARISAR, that achieves both a resolution-preserving denoising and a robust phase unwrapping by combining non-local denoising methods based on patch similarities and total-variation regularization. The optimization algorithm, based on graph-cuts, identifies the global optimum. Combining patch-based speckle reduction methods and regularization-based phase unwrapping requires solving several issues: (i) computational complexity, the inclusion of non-local neighborhoods strongly increasing the number of terms involved during the regularization, and (ii) adaptation to varying neighborhoods, patch comparison leading to large neighborhoods in homogeneous regions and much sparser neighborhoods in some geometrical structures. PARISAR solves both issues. We compare PARISAR with other reconstruction methods both on numerical simulations and satellite images and show a qualitative and quantitative improvement over state-of-the-art reconstruction methods for multi-baseline SAR interferometry.

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Section: Validation On Numerical Simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parisar: Patch-Based Estimation and Regularized Inversion for Multibaseline SAR Interferometry

Ferraioli

Deledalle

Denis

et al. 2018

IEEE Trans. Geosci. Remote Sensing

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“…Examples of multi-channel algorithms include Ghiglia and Wahl (1994), Fornaro et al (2006), Ferraioli et al (2009), andShabou et al (2012). The first two approaches propose maximum likelihood (ML) frameworks for the retrieval of the height, while the third and fourth employ maximum a-posteriori extensions in order to incorporate contextual information.…”

Section: Dual-baseline Region-growing Phase Unwrappingmentioning

confidence: 99%

Large-baseline InSAR for precise topographic mapping: a framework for TanDEM-X large-baseline data

2017

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Abstract. The global Digital Elevation Model (DEM) resulting from the TanDEM-X mission provides information about the world topography with outstanding precision. In fact, performance analysis carried out with the already available data have shown that the global product is well within the requirements of 10 m absolute vertical accuracy and 2 m relative vertical accuracy for flat to moderate terrain. The mission's science phase took place from October 2014 to December 2015. During this phase, bistatic acquisitions with across-track separation between the two satellites up to 3.6 km at the equator were commanded. Since the relative vertical accuracy of InSAR derived elevation models is, in principle, inversely proportional to the system baseline, the TanDEM-X science phase opened the doors for the generation of elevation models with improved quality with respect to the standard product. However, the interferometric processing of the largebaseline data is troublesome due to the increased volume decorrelation and very high frequency of the phase variations. Hence, in order to fully profit from the increased baseline, sophisticated algorithms for the interferometric processing, and, in particular, for the phase unwrapping have to be considered. This paper proposes a novel dual-baseline region-growing framework for the phase unwrapping of the large-baseline interferograms. Results from two experiments with data from the TanDEM-X science phase are discussed, corroborating the expected increased level of detail of the large-baseline DEMs.

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“…To this end, various phase unwrapping methods, techniques and algorithms have been proposed and developed [2] whose main idea is to minimize the phase differences between adjacent pixels: i) either, locally by correcting the phase step by step and imposing certain restrictions on the unwrapping path. [3] [4], ii) or, globally by modeling and optimization of possible solutions [5] [6]. Through this paper, we propose a method of phase unwrapping that combines /fusions the advantages of two methods, namely: The method of phase unwrapping by avoidance of discontinuities, based on creating cut lines.…”

Section: Introductionmentioning

confidence: 99%

2D Phase Unwrapping by fusion Cut-Line and Quality-Maps

Hocine¹,

Belhadj-Aissa²,

Fekir³

et al. 2015

Proceedings of Fringe 2015: Advances in the Science and Applications of SAR Interferometry and Sentinel-1 InSAR Workshop

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Phase unwrapping is one of the critical step in the feasibility of InSAR processes. It consists in retrieving the absolute phase from modulo 2π. Through this paper, we propose a method of phase unwrapping that combines /fusions the advantages of two methods, namely: The method of phase unwrapping by avoidance of discontinuities, based on creating cut lines and the second method, it is based on local/regional propagation of phase unwrapping by referring to quality maps. The idea is that the quality value between the two target edges of the cut-line is set at zero in the quality maps, then the progress of the unwrapping is performed according to the process of the second method where unwrapping phase by referring to edge quality map. To validate the results, the method has been tested using real interferogram.

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Multichannel Phase Unwrapping With Graph Cuts

Cited by 60 publications

References 16 publications

Parisar: Patch-Based Estimation and Regularized Inversion for Multibaseline SAR Interferometry

Parisar: Patch-Based Estimation and Regularized Inversion for Multibaseline SAR Interferometry

Large-baseline InSAR for precise topographic mapping: a framework for TanDEM-X large-baseline data

2D Phase Unwrapping by fusion Cut-Line and Quality-Maps

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