NAME

s.growing

SYNOPSIS

s.growing
s.growing help
s.growing [-o] input=string first=string output=string [Tj=float] [Td=float]

DESCRIPTION

s.growing allows the filling of the inner part of an object, starting from a site file containing the edges classification output of the command s.edgedetections.

This command is part of a procedure to filter airbone laser scanning (LIDAR) data in order to extract Digital Terrain Model.
The procedure involves the execution of the following commands:
1) s.edgedetection - 2) s.growing - 3) s.correction - 4) s.bspline.reg

example of batch file to process LIDAR data:
g.region site4 res=2
s.edgedetection input=4newLast.txt outClas=4class Sge=4 Sgn=4 lambda_r=2
s.growing input=4class first=4FirstPulse.txt output=4grow
s.correction input=4grow   output=4corr_1 out_ter=4ter Sce=50 Scn=50 lambda_c=5 Tc=1 tc=0.5
s.correction input=4corr_1 output=4corr   out_ter=4ter Sce=50 Scn=50 lambda_c=5 Tc=1 tc=0.5
s.bspline.reg -g input=4ter output=4dtm.rast Sie=4 Sin=4 type=bilinear lambda_i=0.1
s.to.qrast input=4class rast=4class.rast type=max findex=1
s.to.qrast input=4grow  rast=4grow.rast  type=max findex=1
s.to.qrast input=4corr  rast=4corr.rast  type=max findex=1
r.mapcalc 4diff.rast=4newLast.rast-4dtm.rast

OPTIONS

The user can run this program either interactively or non- interactively. The program is ran non-interactively if the user specifies program arguments and flag settings on the command line using the form:

s.growing [-o] input=string first=string output=string [Tj=float] [Td=float]

Alternately, the user can simply type s.growing on the command line without program arguments. In this case, the user will be prompted for parameter values and flag settings using the standard GRASS parser interface described in the manual entry for parser.

Flags:

-o
without region growing procedure?

Parameters:

input=string
input last pulse observation sites list file name
first=string
input first pulse sites list file name (s.bordilaser output)
output=string
output classificated sites list file name
Tj=float
threshold for cell object frequency in region growing
Default: 0.2
Td=float
threshold for double pulse in region growing
Default: 0.6

NOTES

The data are rasterized with a resolution rd equal to the minimum data raw density. The algorithm computes the mean height value of the points belonging to each cell and assignes it this value. For each cell the presence of points with double pulse is evaluated (difference between first and last pulse greater than Td). Starting from the cells classified as ‘edge’ and with only one pulse, all the linked cells are found and a convex hull algorithm (De Berg et al. 2000, O’Rourke 1998) is applied on them, computing at the same time the mean of the corresponding heights (mean edge height). The points inside the convex hull are classified as objects in case their height is greater or equal to the previously mean computed edge height.
Obviously the method proposed works better in case of data with high planimetric resolution and it is practically inapplicable to data with density lower than 0.18 points per square metre, corresponding to a point spacing of about 2.0 - 3.5 m.

Every point of the input site file is examinated and classified as:
·       terrain (0)
·       terrain with double pulse (1)
·       object with double pulse (2)
·       object (3)

input format example (x|y|z):

499827.48|5418882.79|#0 %247.49 %0 %247.06513365
499827.1|5418870.68|#0 %246.7 %0 %246.32005415
499826.72|5418858.43|#0 %245.7 %1 %245.59059607
499826.33|5418846.07|#0 %244.65 %0 %244.9670436
499825.96|5418833.6|#0 %244.23 %2 %244.52604082

first format example (x|y|z):

499449.89|5418727.62|#3 %254.70
499449.44|5418723.09|#4 %260.55
499449.63|5418721.88|#5 %255.19
499449.56|5418648.45|#6 %254.33
499449.89|5418652.11|#7 %250.67
499449.74|5418653.86|#8 %256.03

output format example (x|y|z%classification%bicubic_interpolated_value):
 
499453.39|5418722.51|#0 %245.99 %1
499453.52|5418725.56|#0 %246.19 %1
499453.67|5418728.67|#0 %246.2 %2
499453.81|5418731.8|#0 %246.2 %3
499453.9|5418734.65|#0 %247.13 %0
499454.66|5418734.31|#0 %246.99 %0 

SEE ALSO

s.correction, s.bspline, s.edgedetection, s.to.qrast

AUTHORS

Maria A. Brovelli, Politecnico di Milano - Campus Como
Massimiliano Cannata, Politecnico di Milano - Campus Como
Ulisse M. Longoni, Politecnico di Milano - Campus Como

REFERENCES

Brovelli M.A.; Cannata M.; Longoni U.M., LIDAR Data Filtering and DTM Interpolation Within GRASS
 Transactions in GIS, April 2004, vol. 8, no. 2, pp. 155-174(20)  - Blackwell Publishing Ltd

De Berg M, van Kreveld M, Overmars M and Schwarzkopf 2000 Computer Geometry: algorithms and applications. Uthrecht, The Netherlands. Springer Verlag