Contextual Information-Based Multichannel Synthetic Aperture Radar Interferometry: Addressing DEM reconstruction using contextual information

Baselice, Fabio; Ferraioli, Giampaolo; Pascazio, Vito; Schirinzi, Gilda

doi:10.1109/msp.2014.2312282

Cited by 57 publications

(25 citation statements)

References 25 publications

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“…The visual analysis is confirmed by the quantitative analysis based on the evaluation of the Normalized Reconstruction Square Error, defined as the quadratic norm of the difference between the true height values and the estimated ones, normalized by the the true ones (see [22]) of Table II and on the Root Mean Square Error, reported in Table III. For both parameters, PARISAR outperforms the other algorithms.…”

Section: A Urban-like Profilementioning

confidence: 63%

“…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: A Urban-like Profilementioning

confidence: 63%

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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“…[19][20][21] Besides, other phase unwrapping methodologies are developed based on statistical approaches in a multi-channel framework by using a single interferogram. [1,25,25,[27][28][29] C ¼ …”

Section: Joint Phase Unwrappingmentioning

confidence: 99%

A novel SAR interferometry processing method in high resolution spotlight SAR

2015

Journal of Electromagnetic Waves and Applications

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Synthetic Aperture Radar Interferometry (InSAR) can produce accurate Digital Elevation Model (DEM), and a novel spotlight SAR can get higher azimuth resolution than conventional strip-map SAR. Hence, the combination of InSAR and spotlight SAR techniques implies the potential to obtain higher resolution DEMs which will show more tiny terrain variations. However, in spotlight InSAR processing, additional azimuth interferometric fringes and misregistration decorrelation will be induced by conventional co-registration methods which ignore azimuth varied Doppler centroids in interpolations. Meanwhile, because interferometric fringes in regions with high resolution terrain variations can easily be damaged by random phase noise, there will generate more unreliable unwrapped phase areas with conventional phase unwrapping methods. To eliminate residual azimuth fringes, misregistration decorrelation and unreliable unwrapped phase areas, this paper firstly proposes a novel spotlight SAR interferometry processing method based on co-registration related to two-dimensional Doppler centroid estimation and joint phase unwrapping, and it is validated by using TerraSAR-X spotlight data. The final generated 4 m × 4 m grid space DEM not only preserves high resolution, but also has low root mean square error and absolute mean error which are less than 3 m with respect to the Google earth DEM.

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“…Multi-channel Synthetic Aperture Radar (SAR) interferometry allows accurate reconstructions of the height profile of the observed scenes [1][2][3]. Spaceborne differential interferometric SAR (DInSAR) [4] involves the exploitation of multiple interferograms computed from pairs of complex SAR images and aims at measuring surface deformations.…”

Section: Introductionmentioning

confidence: 99%

Extension of a Fast GLRT Algorithm to 5D SAR Tomography of Urban Areas

2017

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This paper analyzes a method for Synthetic Aperture Radar (SAR) Tomographic (TomoSAR) imaging, allowing the detection of multiple scatterers that can exhibit time deformation and thermal dilation by using a CFAR (Constant False Alarm Rate) approach. In the last decade, several methods for TomoSAR have been proposed. The objective of this paper is to present the results obtained on high resolution tomographic SAR data of urban areas, by using a statistical test for detecting multiple scatterers that takes into account phase variations due to possible deformations and/or thermal dilation. The test can be evaluated in terms of probability of detection (P D ) and probability of false alarm (P FA ), and is based on an approximation of a Generalized Likelihood Ratio Test (GLRT), denoted as Fast-Sup-GLRT. It was already applied and validated by the authors in the 3D case, while here it is extended and experimented in the 5D case. Numerical experiments on simulated and on StripMap TerraSAR-X (TSX) data have been carried out. The presented results show that the adopted method allows the detection of a large number of scatterers and the estimation of their position with a good accuracy, and that the consideration of the thermal dilation and surface deformation helps in recovering more single and double scatterers, with respect to the case in which these contributions are not taken into account. Moreover, the capability of method to provide reliable estimates of the deformations in urban structure suggests its use in structure stress monitoring.

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Contextual Information-Based Multichannel Synthetic Aperture Radar Interferometry: Addressing DEM reconstruction using contextual information

Cited by 57 publications

References 25 publications

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

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

A novel SAR interferometry processing method in high resolution spotlight SAR

Extension of a Fast GLRT Algorithm to 5D SAR Tomography of Urban Areas

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