xenial (1) v.lidar.edgedetection.1grass.gz

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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
           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

       --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

       ns_step=float
           Length of each spline step in the north-south 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 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: 2015-10-09 20:24:22 +0200 (Fri, 09 Oct 2015) $

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