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) $
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