DEM Reconstruction Accuracy in Multichannel SAR Interferometry

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

doi:10.1109/tgrs.2008.2002644

Cited by 88 publications

(57 citation statements)

References 18 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%

“…In this regard, multichannel MAP height estimator algorithm with phase unwrapping can produce accurately digital elevation model (DEM) of shoreline deformation. In doing so, this study extends the previous work was done by Baseline et al (2009), Ferraiuolo et al (2009) and Marghany (2012 by implementing a GMRF technique with InSAR procedures. Further, it uses three C-band SAR images acquired by an ASAR sensor on board satellite ENVISAT simulate three-dimensional (3-D) shoreline rate of changes.…”

Section: Introductionsupporting

confidence: 82%

“…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: Introductionsupporting

confidence: 82%

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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“…ML approaches are able to provide good height estimates, but their performance can be severely impacted if only a small number of channels is available. The use of contextual information, e.g., in a maximum a posteriori (MAP) framework, can boost the performance, usually at the expenses of computation cost (Ferraiuolo et al, 2009). …”

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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“…Multiple interferograms, commonly known as multi-channel interferograms, can be obtained in two different ways: using sensors working at different frequencies or using sensors acquiring the scene with different baselines. The latter, multi-baseline interferometry, is the case when the sensor observes the same scene, repeatedly, from slightly different positions, and is commonly the adopted one [7].…”

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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DEM Reconstruction Accuracy in Multichannel SAR Interferometry

Cited by 88 publications

References 18 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

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

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

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