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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   [ew_step=float]    [ns_step=float]
       [lambda_g=float]     [tgh=float]     [tgl=float]     [theta_g=float]      [lambda_r=float]
       [--overwrite]  [--help]  [--verbose]  [--quiet]  [--ui]

   Flags:
       -e
           Estimate point density and distance and quit
           Estimate  point  density  and distance in map units for the input vector points within
           the current region extents and quit

       --overwrite
           Allow output files to overwrite existing files

       --help
           Print usage summary

       --verbose
           Verbose module output

       --quiet
           Quiet module output

       --ui
           Force launching GUI dialog

   Parameters:
       input=name [required]
           Name of input vector map
           Or data source for direct OGR access

       output=name [required]
           Name for output vector map

       ew_step=float
           Length of each spline step in the east-west direction
           Default: 4 * east-west resolution

       ns_step=float
           Length of each spline step in the north-south direction
           Default: 4 * north-south resolution

       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  length (in mapping units) of each spline step is defined by ew_step for the east-west
       direction and ns_step for the north-south direction.

       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
       # last return points
       v.lidar.edgedetection input=vector_last output=edge ew_step=8 ns_step=8 lambda_g=0.5

   Complete workflow
       # region settings (using an existing raster)
       g.region raster=elev_lid792_1m
       # import
       v.in.lidar -tr input=points.las output=points
       v.in.lidar -tr input=points.las output=points_first return_filter=first
       # detection
       v.lidar.edgedetection input=points output=edge ew_step=8 ns_step=8 lambda_g=0.5
       v.lidar.growing input=edge output=growing first=points_first
       v.lidar.correction input=growing output=correction terrain=only_terrain
       # visualization of selected points
       # zoom somewhere first, to make it faster
       d.rast map=orthophoto
       d.vect map=correction layer=2 cats=2,3,4 color=red size=0.25
       d.vect map=correction layer=2 cats=1 color=0:128:0 size=0.5
       # interpolation (this may take some time)
       v.surf.rst input=only_terrain elevation=terrain
       # get object points for 3D visualization
       v.extract input=correction layer=2 cats=2,3,4 output=objects

       Figure 1: Example output from complete workflow (red: objects, green: terrain)

        Figure 2: 3D visualization of filtered object  points  (red)  and  terrain  created  from
       terrain points (gray)

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.

SEE ALSO

        v.lidar.growing, v.lidar.correction, v.surf.bspline, v.surf.rst, v.in.lidar, v.in.ascii

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

       Last changed: $Date: 2019-01-30 12:02:35 +0100 (Wed, 30 Jan 2019) $

SOURCE CODE

       Available at: v.lidar.edgedetection source code (history)

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