Urban Digital Elevation Model Reconstruction Using Very High Resolution Multichannel InSAR Data

Shabou, Aymen; Baselice, Fabio; Ferraioli, Giampaolo

doi:10.1109/tgrs.2012.2191155

Cited by 53 publications

(29 citation statements)

References 38 publications

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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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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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“…The use of Kalman Filter in case of multiple acquisitions has been investigated in [16]. Finally, multi-baseline interferograms have also been used together with other information to improve reconstruction accuracy in urban areas: in [17] multibaseline data have been jointly processed with multi-aspect data while in [18] multi-baseline interferograms have been exploited together with amplitude information.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, ambiguities due to phase wrapping can often be solved based on local smoothness priors. Markovian prior models of the elevation can be defined in this regard: total variation (TV) or truncated quadratic functions lead to smooth elevation while allowing strong discontinuities [18]. These regularization models applied alone suffer some limits like staircasing effects affecting low slope areas and leading to piecewise constant reconstruction [26].…”

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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“…Scattering mechanisms are very complex in urban areas due to multiple scattering by man-made structures (Margarit, Mallorquí, & Pipia, 2010). Urban digital elevation models (DEM) estimated by InSAR are thus generally not accurate, but several approaches to improving accuracy have been presented (Shabou, Baselice, & Ferraioli, 2012;Thiele, Cadario, Schulz, Thönnessen, & Soergel, 2007). Permanent scatter InSAR (PSInSAR) (Ferretti, Prati, & Rocca, 2001) and SqueeSAR (Ferretti et al, 2011) generate DEM with very high accuracy (millimeter scale), even for urban areas (Chaussard, Wdowinski, Cabral-Cano, & Amelung, 2014;Ferretti, Prati, & Rocca, 2000;Perissin & Wang, 2012;Stramondo et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Urban density mapping of global megacities from polarimetric SAR images

Susaki

Kajimoto²,

Kishimoto

2014

Remote Sensing of Environment

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We propose an algorithm for estimating urban density from polarimetric synthetic aperture radar (SAR) images, and compare the urban density patterns of global megacities. SAR images are uniquely able to detect structural information of objects, but they are very sensitive to orientation angle. This issue has been an obstacle to applying SAR images to urban areas. Kajimoto and Susaki (2013b) proposed an algorithm to handle this issue. The effects of polarization orientation angle (POA) are removed by rotating the coherency matrix and then calculating the mean and standard deviation of scattering power by POA domain. The algorithm can estimate urban density from a single fully polarimetric SAR image but has the drawback that the generated urban density maps of multiple images are not comparable with each other because the algorithm generates a relative urban density valid only within the analyzed image. We therefore extend the method by calculating POA-domain statistics from all images of interest so that the generated maps can be compared. Estimated urban densities are assessed on two types of urban density generated from GIS data, building-to-land ratio and floor-area ratio. We demonstrate that the extended method can estimate urban density with reasonable accuracy. Finally, we generate two scattergrams of indices derived from urban density maps of global megacities. An analysis using the scattergrams indicates insightful information about the patterns of urban development. We conclude that the proposed algorithm and the analysis using the obtained results are beneficial to understanding the conditions in megacities.

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Urban Digital Elevation Model Reconstruction Using Very High Resolution Multichannel InSAR Data

Cited by 53 publications

References 38 publications

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

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

Urban density mapping of global megacities from polarimetric SAR images

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