1v.lidar.edgedetection(1) GRASS GIS 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 [-e] input=name output=name [ew_step=float]
16 [ns_step=float] [lambda_g=float] [tgh=float] [tgl=float]
17 [theta_g=float] [lambda_r=float] [--overwrite] [--help] [--ver‐
18 bose] [--quiet] [--ui]
19 Flags:
21 -e
22 Estimate point density and distance and quit
23 Estimate point density and distance in map units for the input vec‐
24 tor points within the current region extents and quit
25 --overwrite
27 Allow output files to overwrite existing files
28 --help
30 Print usage summary
31 --verbose
33 Verbose module output
34 --quiet
36 Quiet module output
37 --ui
39 Force launching GUI dialog
40 Parameters:
42 input=name [required]
43 Name of input vector map
44 Or data source for direct OGR access
45 output=name [required]
47 Name for output vector map
48 ew_step=float
50 Length of each spline step in the east-west direction
51 Default: 4 * east-west resolution
52 ns_step=float
54 Length of each spline step in the north-south direction
55 Default: 4 * north-south resolution
56 lambda_g=float
58 Regularization weight in gradient evaluation
59 Default: 0.01
60 tgh=float
62 High gradient threshold for edge classification
63 Default: 6
64 tgl=float
66 Low gradient threshold for edge classification
67 Default: 3
68 theta_g=float
70 Angle range for same direction detection
71 Default: 0.26
72 lambda_r=float
74 Regularization weight in residual evaluation
75 Default: 2
76
78 v.lidar.edgedetection is the first of three steps to filter LiDAR data.
79 The filter aims to recognize and extract attached and detached object
80 (such as buildings, bridges, power lines, trees, etc.) in order to
81 create a Digital Terrain Model.
82 In particular, this module detects the edge of each single feature over
84 the terrain surface of a LIDAR point surface. First of all, a bilinear
85 spline interpolation with a Tychonov regularization parameter is per‐
86 formed. The gradient is minimized and the low Tychonov regularization
87 parameter brings the interpolated functions as close as possible to the
88 observations. Bicubic spline interpolation with Tychonov regularization
89 is then performed. However, now the curvature is minimized and the reg‐
90 ularization parameter is set to a high value. For each point, an inter‐
91 polated value is computed from the bicubic surface and an interpolated
92 gradient is computed from the bilinear surface. At each point the gra‐
93 dient magnitude and the direction of the edge vector are calculated,
94 and the residual between interpolated and observed values is computed.
95 Two thresholds are defined on the gradient, a high threshold tgh and a
96 low one tgl. For each point, if the gradient magnitude is greater than
97 or equal to the high threshold and its residual is greater than or
98 equal to zero, it is labeled as an EDGE point. Similarly a point is la‐
99 beled as being an EDGE point if the gradient magnitude is greater than
100 or equal to the low threshold, its residual is greater than or equal to
101 zero, and the gradient to two of eight neighboring points is greater
102 than the high threshold. Other points are classified as TERRAIN.
103 The length (in mapping units) of each spline step is defined by ew_step
105 for the east-west direction and ns_step for the north-south direction.
106 The output will be a vector map in which points has been classified as
108 TERRAIN, EDGE or UNKNOWN. This vector map should be the input of v.li‐
109 dar.growing module.
110
112 In this module, an external table will be created which will be useful
113 for the next module of the procedure of LiDAR data filtering. In this
114 table the interpolated height values of each point will be recorded.
115 Also points in the output vector map will be classified as:
116 TERRAIN (cat = 1, layer = 1)
117 EDGE (cat = 2, layer = 1)
118 UNKNOWN (cat = 3, layer = 1)
119 The final result of the whole procedure (v.lidar.edgedetection, v.li‐
120 dar.growing, v.lidar.correction) will be a point classification in four
121 categories:
122 TERRAIN SINGLE PULSE (cat = 1, layer = 2)
123 TERRAIN DOUBLE PULSE (cat = 2, layer = 2)
124 OBJECT SINGLE PULSE (cat = 3, layer = 2)
125 OBJECT DOUBLE PULSE (cat = 4, layer = 2)
126
128 Basic edge detection
129 # last return points
130 v.lidar.edgedetection input=vector_last output=edge ew_step=8 ns_step=8 lambda_g=0.5
131 Complete workflow
133 # region settings (using an existing raster)
134 g.region raster=elev_lid792_1m
135 # import
136 v.in.lidar -tr input=points.las output=points
137 v.in.lidar -tr input=points.las output=points_first return_filter=first
138 # detection
139 v.lidar.edgedetection input=points output=edge ew_step=8 ns_step=8 lambda_g=0.5
140 v.lidar.growing input=edge output=growing first=points_first
141 v.lidar.correction input=growing output=correction terrain=only_terrain
142 # visualization of selected points
143 # zoom somewhere first, to make it faster
144 d.rast map=orthophoto
145 d.vect map=correction layer=2 cats=2,3,4 color=red size=0.25
146 d.vect map=correction layer=2 cats=1 color=0:128:0 size=0.5
147 # interpolation (this may take some time)
148 v.surf.rst input=only_terrain elevation=terrain
149 # get object points for 3D visualization
150 v.extract input=correction layer=2 cats=2,3,4 output=objects
151 Figure 1: Example output from complete workflow (red: objects, green:
153 terrain)
154 Figure 2: 3D visualization of filtered object points (red) and terrain
156 created from terrain points (gray)
157
159 • Antolin, R. et al., 2006. Digital terrain models determination
160 by LiDAR technology: Po basin experimentation. Bolletino di
161 Geodesia e Scienze Affini, anno LXV, n. 2, pp. 69-89.
162 • Brovelli M. A., Cannata M., Longoni U.M., 2004. LIDAR Data Fil‐
164 tering and DTM Interpolation Within GRASS, Transactions in GIS,
165 April 2004, vol. 8, iss. 2, pp. 155-174(20), Blackwell Pub‐
166 lishing Ltd.
167 • Brovelli M. A., Cannata M., 2004. Digital Terrain model recon‐
169 struction in urban areas from airborne laser scanning data: the
170 method and an example for Pavia (Northern Italy). Computers
171 and Geosciences 30 (2004) pp.325-331
172 • Brovelli M. A. and Longoni U.M., 2003. Software per il filtrag‐
174 gio di dati LIDAR, Rivista dell’Agenzia del Territorio, n.
175 3-2003, pp. 11-22 (ISSN 1593-2192).
176 • Brovelli M. A., Cannata M. and Longoni U.M., 2002. DTM LIDAR in
178 area urbana, Bollettino SIFET N.2, pp. 7-26.
179 • Performances of the filter can be seen in the ISPRS WG III/3
181 Comparison of Filters report by Sithole, G. and Vosselman, G.,
182 2003.
183
185 v.lidar.growing, v.lidar.correction, v.surf.bspline, v.surf.rst,
186 v.in.lidar, v.in.ascii
187
189 Original version of program in GRASS 5.4:
190 Maria Antonia Brovelli, Massimiliano Cannata, Ulisse Longoni and Mirko
191 Reguzzoni
192 Update for GRASS 6.X:
193 Roberto Antolin and Gonzalo Moreno
194
196 Available at: v.lidar.edgedetection source code (history)
197 Accessed: Saturday Oct 28 18:18:36 2023
199 Main index | Vector index | Topics index | Keywords index | Graphical
201 index | Full index
202 © 2003-2023 GRASS Development Team, GRASS GIS 8.3.1 Reference Manual
204GRASS 8.3.1 v.lidar.edgedetection(1)