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


       vector, LIDAR, edges


       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]

           Estimate point density and distance
           Estimate  point  density  and  distance for the input vector points within the current
           region extends and quit

           Allow output files to overwrite existing files

           Verbose module output

           Quiet module output

           Name of input vector map

           Name for output vector map

           Interpolation spline step value in east direction
           Default: 4

           Interpolation spline step value in north direction
           Default: 4

           Regularization weight in gradient evaluation
           Default: 0.01

           High gradient threshold for edge classification
           Default: 6

           Low gradient threshold for edge classification
           Default: 3

           Angle range for same direction detection
           Default: 0.26

           Regularization weight in residual evaluation
           Default: 2


       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.


       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)


   Basic edge detection

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


        v.lidar.growing, v.lidar.correction,


       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


       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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