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v.lidar.edgedetection

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v.lidar.edgedetection: Detects the object's edges from a LIDAR data set.

NAME

v.lidar.edgedetection - Detects the object's edges from a LIDAR data set.
KEYWORDS
vector, LIDAR, edges
SYNOPSIS
v.lidar.edgedetection v.lidar.edgedetection help v.lidar.edgedetection [-e] input=name output=name [see=float] [sen=float] [lambda_g=float] [tgh=float] [tgl=float] [theta_g=float] [lambda_r=float] [--overwrite] [--verbose] [--quiet] Flags: -e Estimate point density and distance Estimate point density and distance for the input vector points within the current region extends and quit --overwrite Allow output files to overwrite existing files --verbose Verbose module output --quiet Quiet module output Parameters: input=name Name of input vector map output=name Name for output vector map see=float Interpolation spline step value in east direction Default: 4 sen=float Interpolation spline step value in north direction Default: 4 lambda_g=float Regularization weight in gradient evaluation Default: 0.01 tgh=float High gradient threshold for edge classification Default: 6 tgl=float Low gradient threshold for edge classification Default: 3 theta_g=float Angle range for same direction detection Default: 0.26 lambda_r=float Regularization weight in residual evaluation Default: 2

DESCRIPTION

v.lidar.edgedetection is the first of three steps to filter LiDAR data. The filter aims to recognize and extract attached and detached object (such as buildings, bridges, power lines, trees, etc.) in order to create a Digital Terrain Model. In particular, this module detects the edge of each single feature over the terrain surface of a LIDAR point surface. First of all, a bilinear spline interpolation with a Tychonov regularization parameter is performed. The gradient is minimized and the low Tychonov regularization parameter brings the interpolated functions as close as possible to the observations. Bicubic spline interpolation with Tychonov regularization is then performed. However, now the curvature is minimized and the regularization parameter is set to a high value. For each point, an interpolated value is computed from the bicubic surface and an interpolated gradient is computed from the bilinear surface. At each point the gradient magnitude and the direction of the edge vector are calculated, and the residual between interpolated and observed values is computed. Two thresholds are defined on the gradient, a high threshold tgh and a low one tgl. For each point, if the gradient magnitude is greater than or equal to the high threshold and its residual is greater than or equal to zero, it is labeled as an EDGE point. Similarly a point is labeled as being an EDGE point if the gradient magnitude is greater than or equal to the low threshold, its residual is greater than or equal to zero, and the gradient to two of eight neighboring points is greater than the high threshold. Other points are classified as TERRAIN. The output will be a vector map in which points has been classified as TERRAIN, EDGE or UNKNOWN. This vector map should be the input of v.lidar.growing module.

NOTES

In this module, an external table will be created which will be useful for the next module of the procedure of LiDAR data filtering. In this table the interpolated height values of each point will be recorded. Also points in the output vector map will be classified as: TERRAIN (cat = 1, layer = 1) EDGE (cat = 2, layer = 1) UNKNOWN (cat = 3, layer = 1) The final result of the whole procedure (v.lidar.edgedetection, v.lidar.growing, v.lidar.correction) will be a point classification in four categories: TERRAIN SINGLE PULSE (cat = 1, layer = 2) TERRAIN DOUBLE PULSE (cat = 2, layer = 2) OBJECT SINGLE PULSE (cat = 3, layer = 2) OBJECT DOUBLE PULSE (cat = 4, layer = 2)

EXAMPLES

Basic edge detection v.lidar.edgedetection input=vector_last output=edge see=8 sen=8 lambda_g=0.5

SEE ALSO

v.lidar.growing, v.lidar.correction, v.surf.bspline
AUTHORS
Original version of program in GRASS 5.4: Maria Antonia Brovelli, Massimiliano Cannata, Ulisse Longoni and Mirko Reguzzoni Update for GRASS 6.X: Roberto Antolin and Gonzalo Moreno
REFERENCES
Antolin, R. et al., 2006. Digital terrain models determination by LiDAR technology: Po basin experimentation. Bolletino di Geodesia e Scienze Affini, anno LXV, n. 2, pp. 69-89. Brovelli M. A., Cannata M., Longoni U.M., 2004. LIDAR Data Filtering and DTM Interpolation Within GRASS, Transactions in GIS, April 2004, vol. 8, iss. 2, pp. 155-174(20), Blackwell Publishing Ltd. Brovelli M. A., Cannata M., 2004. Digital Terrain model reconstruction in urban areas from airborne laser scanning data: the method and an example for Pavia (Northern Italy). Computers and Geosciences 30 (2004) pp.325-331 Brovelli M. A. and Longoni U.M., 2003. Software per il filtraggio di dati LIDAR, Rivista dell?Agenzia del Territorio, n. 3-2003, pp. 11-22 (ISSN 1593-2192). Brovelli M. A., Cannata M. and Longoni U.M., 2002. DTM LIDAR in area urbana, Bollettino SIFET N.2, pp. 7-26. Performances of the filter can be seen in the ISPRS WG III/3 Comparison of Filters report by Sithole, G. and Vosselman, G., 2003. Last changed: $Date: 2010-09-16 09:25:59 +0200 (Thu, 16 Sep 2010) $ Full index (C) 2003-2010 GRASS Development Team V.LIDAR.EDGEDETECTION(1)
 
 
 

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