A Markovian Approach for DEM Estimation From Multiple InSAR Data With Atmospheric Contributions

Shabou, Aymen; Tupin, Florence

doi:10.1109/lgrs.2011.2181326

Cited by 12 publications

(4 citation statements)

References 11 publications

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“…There are many ways to obtain DEMs, such as InSAR, photogrammetry and LiDAR [43,44,[66][67][68]. As an indispensable product in most areas of earth science, the DEM should be chosen carefully in terms of its resolution and accuracy.…”

Section: Selection Of Dem For Inundation Assessmentmentioning

confidence: 99%

Coastal Dam Inundation Assessment for the Yellow River Delta: Measurements, Analysis and Scenario

Wang

Ding

et al. 2020

Remote Sensing

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Coastal dams along the Yellow River Delta are built to prevent seawater intrusion. However, land subsidence caused by significant oil, gas and brine extraction, as well as sediment compaction, could exacerbate the flooding effects of sea-level rise and storm surge. In order to evaluate the coastal dam vulnerability, we combined unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR) with small baseline subsets (SBAS) interferometric synthetic aperture radar (InSAR) results to generate an accurate coastal dam digital elevation model (DEM) over the next 10, 30 and 80 years. Sea-level simulation was derived from the relative sea-level rise scenarios published by the Intergovernmental Panel on Climate Change (IPCC) and local long-term tide gauge records. Assuming that the current rate of dam vertical deformation and sea-level rise are linear, we then generated different inundation scenarios by the superposition of DEMs and sea-levels at different periods by way of a bathtub model. We found that the overtopping event would likely occur around Year 2050, and the northern part of the dam would lose its protective capability almost entirely by the end of this century. This article provides an alternative cost-effective method for the detection, extraction and monitoring of coastal artificial infrastructure.

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Section: Selection Of Dem For Inundation Assessmentmentioning

confidence: 99%

Coastal Dam Inundation Assessment for the Yellow River Delta: Measurements, Analysis and Scenario

Wang

Ding

et al. 2020

Remote Sensing

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“…Concerning the first step, different techniques can be applied according to the extension and to the topography of the observed scene. The algorithm proposed in [43] has been adopted for Napoli test case, while the algorithm proposed in [44] has been considered for SerrePonçon test case. Concerning the second step, commonly a relative phase calibration is applied based on the identification of high coherence areas or permanent scatters.…”

Section: Application To Satellite Sar Imagesmentioning

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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“…However, the assumptions of zero ground deformation and statistically independent IFMs, do not always hold true. MAP is characterized by good performances [25], [26]; MAP combined with Markov random fields [29] can recover topographic profiles affected by strong height discontinuities and noise can be rejected efficiently, yet is limited by the heavy computational burden.…”

Section: Introductionmentioning

confidence: 99%

A New Baseline Linear Combination Algorithm for Generating Urban Digital Elevation Models With Multitemporal InSAR Observations

Luo

Dong

et al. 2020

IEEE Trans. Geosci. Remote Sensing

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The lack of high-resolution digital elevation model (DEM) data presents one major limitation for deformation mapping using synthetic aperture radar interferometry (InSAR) techniques with high-spatial-resolution radar imagery (e.g. TerraSAR-X). This paper presents a baseline linear combination (BLC) approach to generate interferograms with nearly-zero-baselines so as to minimize the effects of the uncertainties in the DEM used. It incorporates the baseline combination method with adjacent gradient networking to successfully unwrap the interferograms even in abruptly discontinuous areas, which in turn can be used to estimate a high-resolution DEM. The BLC approach does not require any deformation model; instead, it utilizes nearly-zerobaseline interferograms to assist with 3D phase unwrapping. Application of the BLC approach to the TerraSAR-X dataset in Shenzhen city, China shows that the BLC derived DEM agrees with the digital surface model (DSM) obtained from light detection and ranging (LiDAR) with a correlation coefficient of 0.998 and a root mean square error (RMSE) of 2.05m, demonstrating the effectiveness of the BLC approach. Note that the BLC approach is not only able to be employed in urban areas with high buildings, but also in mountain areas with steep slopes. Index Terms-3D phase unwrapping, digital elevation model (DEM), multitemporal synthetic aperture radar interferometry (MTInSAR)with the College of Geological Engineering and Geomatics, Chang'an University,

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A Markovian Approach for DEM Estimation From Multiple InSAR Data With Atmospheric Contributions

Cited by 12 publications

References 11 publications

Coastal Dam Inundation Assessment for the Yellow River Delta: Measurements, Analysis and Scenario

Coastal Dam Inundation Assessment for the Yellow River Delta: Measurements, Analysis and Scenario

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

A New Baseline Linear Combination Algorithm for Generating Urban Digital Elevation Models With Multitemporal InSAR Observations

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