scholarly journals Coherent Markov Random Field-Based Unreliable DSM Areas Segmentation and Hierarchical Adaptive Surface Fitting for InSAR DEM Reconstruction

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1414 ◽  
Author(s):  
Qian Qian ◽  
Bingnan Wang ◽  
Xiaoning Hu ◽  
Maosheng Xiang
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Surface Fitting ◽  
Grid Size ◽  
Surface Model ◽  
Reconstruction Method ◽  
Markov Random ◽  
Distance Weighted ◽  
Natural Terrain ◽  
Set Up

A digital elevation model (DEM) can be obtained by removing ground objects, such as buildings, in a digital surface model (DSM) generated by the interferometric synthetic aperture radar (InSAR) system. However, the imaging mechanism will cause unreliable DSM areas such as layover and shadow in the building areas, which seriously affect the elevation accuracy of the DEM generated from the DSM. Driven by above problem, this paper proposed a novel DEM reconstruction method. Coherent Markov random field (CMRF) was first used to segment unreliable DSM areas. With the help of coherence coefficients and residue information provided by the InSAR system, CMRF has shown better segmentation results than traditional traditional Markov random field (MRF) which only use fixed parameters to determine the neighborhood energy. Based on segmentation results, the hierarchical adaptive surface fitting (with gradually changing the grid size and adaptive threshold) was set up to locate the non-ground points. The adaptive surface fitting was superior to the surface fitting-based method with fixed grid size and threshold of height differences. Finally, interpolation based on an inverse distance weighted (IDW) algorithm combining coherence coefficient was performed to reconstruct a DEM. The airborne InSAR data from the Institute of Electronics, Chinese Academy of Sciences has been researched, and the experimental results show that our method can filter out buildings and identify natural terrain effectively while retaining most of the terrain features.

scholarly journals MRI Reconstruction Using Markov Random Field and Total Variation as Composite Prior

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3185
Author(s):  
Marko Panić ◽  
Dušan Jakovetić ◽  
Dejan Vukobratović ◽  
Vladimir Crnojević ◽  
Aleksandra Pižurica
Keyword(s):  
Random Field ◽  
Total Variation ◽  
Markov Random Field ◽  
Adaptive Estimation ◽  
A Priori ◽  
Reconstruction Algorithm ◽  
Original Data ◽  
Magnetic Resonance Images ◽  
Reconstruction Method ◽  
Markov Random

Reconstruction of magnetic resonance images (MRI) benefits from incorporating a priori knowledge about statistical dependencies among the representation coefficients. Recent results demonstrate that modeling intraband dependencies with Markov Random Field (MRF) models enable superior reconstructions compared to inter-scale models. In this paper, we develop a novel reconstruction method, which includes a composite prior based on an MRF model and Total Variation (TV). We use an anisotropic MRF model and propose an original data-driven method for the adaptive estimation of its parameters. From a Bayesian perspective, we define a new position-dependent type of regularization and derive a compact reconstruction algorithm with a novel soft-thresholding rule. Experimental results show the effectiveness of this method compared to the state of the art in the field.

scholarly journals ANALYSIS AND VALIDATION OF GRID DEM GENERATION BASED ON GAUSSIAN MARKOV RANDOM FIELD

2016 ◽  
Vol XLI-B2 ◽  
pp. 277-284 ◽  
Author(s):  
F. J. Aguilar ◽  
M. A. Aguilar ◽  
J. L. Blanco ◽  
A. Nemmaoui ◽  
A. M. García Lorca
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Digital Terrain Model ◽  
Linear Interpolation ◽  
Surface Model ◽  
Lidar Data ◽  
Mathematical Framework ◽  
Land Use Classification ◽  
Gaussian Markov Random Field ◽  
Markov Random

Digital Elevation Models (DEMs) are considered as one of the most relevant geospatial data to carry out land-cover and land-use classification. This work deals with the application of a mathematical framework based on a Gaussian Markov Random Field (GMRF) to interpolate grid DEMs from scattered elevation data. The performance of the GMRF interpolation model was tested on a set of LiDAR data (0.87 points/m<sup>2</sup>) provided by the Spanish Government (PNOA Programme) over a complex working area mainly covered by greenhouses in Almería, Spain. The original LiDAR data was decimated by randomly removing different fractions of the original points (from 10% to up to 99% of points removed). In every case, the remaining points (scattered observed points) were used to obtain a 1 m grid spacing GMRF-interpolated Digital Surface Model (DSM) whose accuracy was assessed by means of the set of previously extracted checkpoints. The GMRF accuracy results were compared with those provided by the widely known Triangulation with Linear Interpolation (TLI). Finally, the GMRF method was applied to a real-world case consisting of filling the LiDAR-derived DSM gaps after manually filtering out non-ground points to obtain a Digital Terrain Model (DTM). Regarding accuracy, both GMRF and TLI produced visually pleasing and similar results in terms of vertical accuracy. As an added bonus, the GMRF mathematical framework makes possible to both retrieve the estimated uncertainty for every interpolated elevation point (the DEM uncertainty) and include break lines or terrain discontinuities between adjacent cells to produce higher quality DTMs.

scholarly journals ANALYSIS AND VALIDATION OF GRID DEM GENERATION BASED ON GAUSSIAN MARKOV RANDOM FIELD

2016 ◽  
Vol XLI-B2 ◽  
pp. 277-284
Author(s):  
F. J. Aguilar ◽  
M. A. Aguilar ◽  
J. L. Blanco ◽  
A. Nemmaoui ◽  
A. M. García Lorca
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Digital Terrain Model ◽  
Linear Interpolation ◽  
Surface Model ◽  
Lidar Data ◽  
Mathematical Framework ◽  
Land Use Classification ◽  
Gaussian Markov Random Field ◽  
Markov Random

Digital Elevation Models (DEMs) are considered as one of the most relevant geospatial data to carry out land-cover and land-use classification. This work deals with the application of a mathematical framework based on a Gaussian Markov Random Field (GMRF) to interpolate grid DEMs from scattered elevation data. The performance of the GMRF interpolation model was tested on a set of LiDAR data (0.87 points/m<sup>2</sup>) provided by the Spanish Government (PNOA Programme) over a complex working area mainly covered by greenhouses in Almería, Spain. The original LiDAR data was decimated by randomly removing different fractions of the original points (from 10% to up to 99% of points removed). In every case, the remaining points (scattered observed points) were used to obtain a 1 m grid spacing GMRF-interpolated Digital Surface Model (DSM) whose accuracy was assessed by means of the set of previously extracted checkpoints. The GMRF accuracy results were compared with those provided by the widely known Triangulation with Linear Interpolation (TLI). Finally, the GMRF method was applied to a real-world case consisting of filling the LiDAR-derived DSM gaps after manually filtering out non-ground points to obtain a Digital Terrain Model (DTM). Regarding accuracy, both GMRF and TLI produced visually pleasing and similar results in terms of vertical accuracy. As an added bonus, the GMRF mathematical framework makes possible to both retrieve the estimated uncertainty for every interpolated elevation point (the DEM uncertainty) and include break lines or terrain discontinuities between adjacent cells to produce higher quality DTMs.

Fusion Moves for Markov Random Field Optimization

2010 ◽  
Vol 32 (8) ◽  
pp. 1392-1405 ◽  
Author(s):  
Victor Lempitsky ◽  
Carsten Rother ◽  
Stefan Roth ◽  
Andrew Blake
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Markov Random
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