Maximum A Posteriori Estimation of Height Profiles in InSAR Imaging

Ferraiuolo, G.; Pascazio, Vito; Schirinzi, Gilda

doi:10.1109/lgrs.2003.822882

Cited by 138 publications

(66 citation statements)

References 10 publications

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“…Further, Marghany (2012) stated that Gaussian Markov Random Field (GMRF) can correct fringe discontinuities, and provides a large number of interpolated samples over corrupted fringe detail. This work confirms the study of Ferretti et al (2001), Ferraiuolo et al (2004), Baseline et al (2009), Ferraiuolo et al (2009), and Marghany (2012. These suggest that a multichannel MAP height estimator algorithm is an excellent method to 3-D shoreline change rate simulation that is based on DInSAR technique.…”

Section: Multichanal Mapsupporting

confidence: 92%

“…Smooth function is used to resolve phase jump by adding or subtracting multiples of 2 π (Sumantyo et al, 2012). Consequently, multichannel MAP height estimator based on a Gaussian Markov Random (GMRF) has developed by Ferretti et al (2001), Ferraiuolo et al (2004), Baseline et al (2009), Ferraiuolo et al (2009 to solve the uncertainties of DEM reconstruction from InSAR technique. They found that the multichannel MAP height estimator has managed the phase discontinuities and improved the DEM profile.…”

Section: Introductionmentioning

confidence: 99%

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Simulation of Three-Dimensional of coastal erosion using differential interferometric synthetic aperture radar

2013

Global NEST Journal

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This study aims at modelling three-dimensional shoreline change rates using differential interferometric synthetic aperture radar (DInSAR) techinuqe. Neverthless, decorrelation plays significant role to control the accuracy of three dimensional object reconstruction using DInSAR. To solve this problem, multichannel MAP height estimator algorithm is implemented with in ENVISAT ASAR data. Therefore, the proposed method has been applied to coastaline of Johor, Malaysia. The study shows the critical erosion of -3.5 m y -1 with accuracy (RMSE) of ±0.05 m. In addition, the volume rate of shoreline changes of -2343.42 m 3 y -1 corresponds to the lowest digital elevation model (DEM) of 7.4 m. It can be said that accurate rate of shoreline change can be achieved with root mean square error (RMSE) of ±0.05 m using multichannel MAP height estimator algorithm.

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Section: Multichanal Mapsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Simulation of Three-Dimensional of coastal erosion using differential interferometric synthetic aperture radar

2013

Global NEST Journal

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“…We clearly see, for instance, if the absolute value of phase difference between neighboring pixels is greater than π, the phase unwrapping operation becomes an ill-posed problem. In this case, one possible approach to solve the ill-posed problem is the multichannel phase unwrapping (MCPU) [11] technique by exploiting the availability of different and independent interferograms referred to the same scene. In order to combine these different available channels, a statistical approach with Maximum a Posteriori (MAP) estimation is used.…”

Section: Methodsmentioning

confidence: 99%

A graph-cut based algorithm for approximate MRF optimization

Shabou

Tupin

Darbon³

2009

2009 16th IEEE International Conference on Image Processing (ICIP)

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“…An effective way to combine the available multichannel (i.e., multi-baseline) interferometric data is to exploit statistical estimation methods. 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].…”

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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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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Maximum A Posteriori Estimation of Height Profiles in InSAR Imaging

Cited by 138 publications

References 10 publications

Simulation of Three-Dimensional of coastal erosion using differential interferometric synthetic aperture radar

Simulation of Three-Dimensional of coastal erosion using differential interferometric synthetic aperture radar

A graph-cut based algorithm for approximate MRF optimization

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

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