1v.lidar.edgedetection(1) Grass User's Manual v.lidar.edgedetection(1)
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6 v.lidar.edgedetection - Detects the object's edges from a LIDAR data
7 set.
8
10 vector, LIDAR, edges
11
13 v.lidar.edgedetection
14 v.lidar.edgedetection help
15 v.lidar.edgedetection input=name output=name [see=float] [sen=float]
16 [lambda_g=float] [tgh=float] [tgl=float] [theta_g=float]
17 [lambda_r=float] [--overwrite] [--verbose] [--quiet]
18 Flags:
20 --overwrite
21 Allow output files to overwrite existing files
22 --verbose
24 Verbose module output
25 --quiet
27 Quiet module output
28 Parameters:
30 input=name
31 Name of input vector map
32 output=name
34 Name for output vector map
35 see=float
37 Interpolation spline step value in east direction
38 Default: 4
39 sen=float
41 Interpolation spline step value in north direction
42 Default: 4
43 lambda_g=float
45 Regularization weight in gradient evaluation
46 Default: 0.01
47 tgh=float
49 High gradient threshold for edge classification
50 Default: 6
51 tgl=float
53 Low gradient threshold for edge classification
54 Default: 3
55 theta_g=float
57 Angle range for same direction detection
58 Default: 0.26
59 lambda_r=float
61 Regularization weight in residual evaluation
62 Default: 2
63
65 v.lidar.edgedetection is the first of three steps to filter LiDAR data.
66 The filter aims to recognize and extract attached and detached object
67 (such as buildings, bridges, power lines, trees, etc.) in order to
68 create a Digital Terrain Model.
69 In particular, this module detects the edge of each single feature over
70 the terrain surface of a LIDAR point surface. First of all, a bilinear
71 spline interpolation with a Tychonov regularization parameter is per‐
72 formed. The gradient is minimized and the low Tychonov regularization
73 parameter brings the interpolated functions as close as possible to the
74 observations. Bicubic spline interpolation with Tychonov regularization
75 is then performed. However, now the curvature is minimized and the reg‐
76 ularization parameter is set to a high value. For each point, an inter‐
77 polated value is computed from the bicubic surface and an interpolated
78 gradient is computed from the bilinear surface. At each point the gra‐
79 dient magnitude and the direction of the edge vector are calculated,
80 and the residual between interpolated and observed values is computed.
81 Two thresholds are defined on the gradient, a high threshold tgh and a
82 low one tgl. For each point, if the gradient magnitude is greater than
83 or equal to the high threshold and its residual is greater than or
84 equal to zero, it is labeled as an EDGE point. Similarly a point is
85 labeled as being an EDGE point if the gradient magnitude is greater
86 than or equal to the low threshold, its residual is greater than or
87 equal to zero, and the gradient to two of eight neighboring points is
88 greater than the high threshold. Other points are classified as TER‐
89 RAIN.
90 The output eill be a vector map in which points has been classified as
91 TERRAIN, EDGE or UNKNOWN. This vector map should be the input of
92 v.lidar.growing module.
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95 In this module, an external table will be created which will be useful
96 for the next module of the procedure of LiDAR data filtering. In this
97 table the interpolated height values of each point will be recorded.
98 Also points in the output vector map will be classified as:
99 EDGE (cat = 1, layer = 1)
100 TERRAIN (cat = 2, layer = 1)
101 The final result of the whole procedure (v.lidar.edgedetection,
102 v.lidar.growing, v.lidar.correction) will be a point classification in
103 four categories:
104 TERRAIN SINGLE PULSE (cat = 1, layer = 2)
105 TERRAIN DOUBLE PULSE (cat = 2, layer = 2)
106 OBJECT SINGLE PULSE (cat = 3, layer = 2)
107 OBJECT DOUBLE PULSE (cat = 4, layer = 2)
108
110 Basic edge detection
111 v.lidar.edgedetection input=vector_last output=edge see=8 sen=8
113 lambda_g=0.5
114
117 v.lidar.growing, v.lidar.correction, v.surf.bspline
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120 Original version of program in GRASS 5.4:
121 Maria Antonia Brovelli, Massimiliano Cannata, Ulisse Longoni and Mirko
122 Reguzzoni
123 Update for GRASS 6.X:
124 Roberto Antolin and Gonzalo Moreno
125
127 Antolin, R. et al., 2006. Digital terrain models determination by LiDAR
128 technology: Po basin experimentation. Bolletino di Geodesia e Scienze
129 Affini, anno LXV, n. 2, pp. 69-89.
130 Brovelli M. A., Cannata M., Longoni U.M., 2004. LIDAR Data Filtering
131 and DTM Interpolation Within GRASS, Transactions in GIS, April 2004,
132 vol. 8, iss. 2, pp. 155-174(20), Blackwell Publishing Ltd.
133 Brovelli M. A., Cannata M., 2004. Digital Terrain model reconstruction
134 in urban areas from airborne laser scanning data: the method and an
135 example for Pavia (Northern Italy). Computers and Geosciences 30 (2004)
136 pp.325-331
137 Brovelli M. A. and Longoni U.M., 2003. Software per il filtraggio di
138 dati LIDAR, Rivista dell?Agenzia del Territorio, n. 3-2003, pp. 11-22
139 (ISSN 1593-2192).
140 Brovelli M. A., Cannata M. and Longoni U.M., 2002. DTM LIDAR in area
141 urbana, Bollettino SIFET N.2, pp. 7-26.
142 Performances of the filter can be seen in the ISPRS WG III/3 Comparison
143 of Filters report by Sithole, G. and Vosselman, G., 2003.
144 Last changed: $Date: 2007-10-18 15:40:28 +0200 (Thu, 18 Oct 2007) $
146 Full index
148 © 2003-2008 GRASS Development Team
150GRASS 6.3.0 v.lidar.edgedetection(1)