Ubuntu Manpage: 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

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 00:25:59 -0700 (Thu, 16 Sep 2010) $

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