scholarly journals BUILDING ROOF BOUNDARY EXTRACTION FROM LiDAR AND IMAGE DATA BASED ON MARKOV RANDOM FIELD

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 339-344 ◽  
Author(s):  
A. P. Dal Poz ◽  
V. J. M. Fernandes
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Image Data ◽  
Region Growing ◽  
Aerial Images ◽  
Aerial Image ◽  
Lidar Data ◽  
Building Roof ◽  
Markov Random ◽  
Bounding Boxes

In this paper a method for automatic extraction of building roof boundaries is proposed, which combines LiDAR data and highresolution aerial images. The proposed method is based on three steps. In the first step aboveground objects are extracted from LiDAR data. Initially a filtering algorithm is used to process the original LiDAR data for getting ground and non-ground points. Then, a region-growing procedure and the convex hull algorithm are sequentially used to extract polylines that represent aboveground objects from the non-ground point cloud. The second step consists in extracting corresponding LiDAR-derived aboveground objects from a high-resolution aerial image. In order to avoid searching for the interest objects over the whole image, the LiDAR-derived aboveground objects’ polylines are photogrammetrically projected onto the image space and rectangular bounding boxes (sub-images) that enclose projected polylines are generated. Each sub-image is processed for extracting the polyline that represents the interest aboveground object within the selected sub-image. Last step consists in identifying polylines that represent building roof boundaries. We use the Markov Random Field (MRF) model for modelling building roof characteristics and spatial configurations. Polylines that represent building roof boundaries are found by optimizing the resulting MRF energy function using the Genetic Algorithm. Experimental results are presented and discussed in this paper.

scholarly journals Power Line Extraction From Aerial Images Using Object-Based Markov Random Field With Anisotropic Weighted Penalty

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 125333-125356 ◽  
Author(s):  
Le Zhao ◽  
Xianpei Wang ◽  
Hongtai Yao ◽  
Meng Tian ◽  
Zini Jian
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Power Line ◽  
Aerial Images ◽  
Line Extraction ◽  
Markov Random ◽  
Object Based

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.

Sign in / Sign up

Export Citation Format

Share Document