This tutorial describes a hybrid approach for classifying ground points from airborne laser scanning (ALS) data. The method combines progressive triangulated irregular network (TIN) densification and point cloud segmentation. First, outliers are removed from the imported ALS point cloud. Then, segmentation is used to group points. Ground points are classified using TIN densification. Finally, a high resolution digital terrain model (DTM) is generated from the ground points and smoothed.

1. TUTORIAL
Ground Classification
Version 1.0, January 2014
Author: Volker Wichmann

2. This tutorial describes how to perform ground filtering of ALS point
clouds with LiS. LiS supports various filtering methods to classify ground
points and to derive high resolution DTMs from this classification. Here
we will focus on a new hybrid approach, which combines progressive
TIN densification and segmentation­based filtering.
The tutorial includes the following topics: import of the ALS point cloud
from LAZ, removal of outliers (low and air points), point cloud
segmentation and ground filtering. Finally, we will generate a high
resolution raster DTM from the classification result.
Tutorial ­ Ground Classification
Introduction

3. Import LAS/LAZ Files
Input Files c:datatut_class.laz
number of the return
number of returns of
given pulse
intensity
The LAZ file format (laszip) is a compressed version of the ASPRS LAS
format. We will use the Import LAS/LAZ Files module to import the
data. Here, we will only import a selection of the available attributes. In
case you like to get more information about the content of the LAZ file,
you can use the LAS/LAZ Info module.
So let's get started ­ open the module settings of the LASERDATA LIS
Data Import Point Clouds Import LAS/LAZ Files module and adjust
the module settings as shown below. After module execution, the
imported dataset will show up in the Data tab of the Manager window. By
double­clicking on the dataset, you can visualize it in a Map View. As you
can see from the colorization of the dataset, there seem to exit some
outliers in the data. Do a right­click on the dataset in the Data tab and
select Classification Set Range to Standard Deviation (2.0) in order to
discard the outliers while applying the color palette.
Tutorial ­ Ground Classification
Step 1: Import of ALS data from LAZ

4. Point Cloud Editor [interactive]
Point Cloud tut_class
The colorization of the dataset has already indicated that the data
contains some outliers. We will verify this by visualizing the dataset with
the LASERDATA LIS Data Editing Point Cloud Point Cloud Editor
[interactive] module. Adjust the settings as shown below and execute
the module.
You can use the F5 / F6 keys to scale the point size. It is obvious that
there are some serious negative outliers (low points), but there are also
some positive ones (air points). Some of the outliers are isolated points,
but many of them are forming clusters.
Tutorial ­ Ground Classification
Step 2: Outlier removal

5. Remove Low and Air Points (PC)
Point Cloud tut_class
Copy existing attr.
Horizont. Search Radius 2
Search for Point Groups
Max Horizont. Dist. 2
Max Vert. Distance 1
Max. Number of Points 8
Number of Iterations 3
Remove Low Points
DZ Low Points 1
Remove Air Points
DZ Air Points 15
Visual inspection revealed that the data contains serious outliers,
sometimes forming clusters of points. Simple outlier removal tools, which
for example only remove isolated points, will fail to remove these point
clusters. For this reason we will use the LASERDATA LIS Data
Analysis Filtering Point Cloud Remove Low and Air Points (PC)
module. Adjust the module settings as shown below and execute the
module.
With these settings both low and air points, as well as point clusters with
up to 8 points. The computations will be done three times in order to
iteratively remove outliers which approximately share the same x/z
location but different elevation levels.
The computations remove 1720 points from the dataset, which is less
than 0.1% of the points.
Tutorial ­ Ground Classification
Step 2: Outlier removal

6. Segmentation by Planes (PC)
Point Cloud tut_class
Copy existing attr.
Method NN k nearest neighbors
k Nearest Neighbors 20
Maximum Distance Thres.
Maximum Distance 2
Robust Plane Fitting
Maximum Distance 0.3
Minimum Percentage 80
Method Search Point point must be included
in best fit plane3
Search Radius 2.5
Minimum Segment Size 20
Maximum Segment Size 100000
Maximum Normal Diff. 30
Maximum Plane Offset 0.5
Normal Recalc. Interv. 100000
Segment Size
In order to use a hybrid classification approach, using both progressive
TIN densification and point cloud segmentation, we will next use the
LASERDATA LIS Data Analysis Segmentation Segmentation by
Planes (PC) module. Adjust the module settings as shown below and
execute the module.
With these settings the segmentation is done by robust plane fitting.
Points with normal vector differences below 30 degree and with a plane
offset below 0.5m will finally be merged by region growing to the final
segments. By default, each point will get assigned a segment ID. Points
for which no plane could be fitted will get an ID of ­1 and points which
were skipped during region growing will get an ID of 0. That is, valid
segments will have segment IDs greater than 0. Additionally, we will
output the segment size (number of points per segment).
Tutorial ­ Ground Classification
Step 3: Point cloud segmentation

7. Point Cloud Editor [interactive]
Point Cloud tut_class
Random segmentid
After module execution, the point cloud is colored by random using the
segment ID as color attribute. Change to the LASERDATA LIS Data
Editing Point Cloud Point Cloud Editor [interactive] module and do
some additional settings as shown below. Then execute the module.
By providing the segmentid attribute with the Random parameter, we are
able to use the keyboard shortcut q to automatically color the point cloud
segments by random. Each time you press the q key, a new random
palette will be assigned. Please note, that the keyboard shortcuts are
only recognized once you have captured the mouse in the 3D canvas
(do a left­click in the canvas to do so).
You can see that the largest segments are built up of ground points.
Smaller segments are found on roofs. In areas of very high point
densities some segments are formed even in vegetation.
Tutorial ­ Ground Classification
Step 3: Point cloud segmentation

8. Ground Classification (PC)
Point Cloud tut_class
Segment ID segmentid
Segment Size segmentsize
Copy existing attr.
Init Cellsize user defined
User Cellsize 50
Cuttof Cellsize 10
Minimum Edge Length 1
Maximum Angle 30
Maximum Distance 1.5
Z Tolerance 0.3
Complete Ground Seg.
Minimum Segment Size 500
Attach DZ to PC
After point cloud segmentation, we are finally prepared to classify all
ground points. Change to the LASERDATA LIS Data Analysis
Classification Ground Classification (PC) module. Adjust the module
settings as shown below and execute the module.
With these settings the seed generation will start with blocks of 50m
edge length (about the largest building size), and will end as soon as the
block size drops below 10m. Because of the steep topography in parts of
the area under investigation, the TIN densification parameters Maximum
Angle and Maximum Distance have to be set to quite large values. After
the TIN has been constructed, all points with an elevation difference
below the Z Tolerance get labelled as ground. Finally, all segments that
contain a ground point and that have more than 500 points are also
labelled as ground.
Tutorial ­ Ground Classification
Step 4: Ground classification

9. Point Cloud Editor [interactive]
Point Cloud tut_class
Classification grdclass
Random segmentid
In order to validate the classification result, we have a more detailed look
with the LASERDATA LIS Data Editing Point Cloud Point Cloud
Editor [interactive] module. Provide the classification result with the
Classification parameter and execute the module.
By providing the grdclass attribute with the Classification parameter, we
are able to use the keyboard shortcut 5 to automatically color the point
cloud segments by classification. This colors the ground points in brown
and all other points in grey. With the keyboard shortcut 6, only the
ground points are visualized.
As you can see, the ground classification is quite complete, even in very
steep terrain. Also road and river embankments are labelled as ground
completely. This is archived by combining the progressive TIN
densification with the segmentation approach.
Tutorial ­ Ground Classification
Step 4: Ground classification

10. Point Cloud to Grid (TIN)
Point Cloud tut_class
Filter Attribute grdclass
Output grid settings User Defined
Filter Min/Max 2;2
Tile Size 1000
Popup Dialog Output Grid Settings
Cellsize 0.5
In this step, we will create a high resolution raster DTM from the
classification result. We will use the LASERDATA LIS Data Analysis
Conversion Point Cloud Point Cloud to Grid (TIN) module for this
purpose. Adjust the module settings as shown below and execute the
module. A dialog will pop up, providing setting options for the output grid.
Here, we will use a Cellsize of 0.5m.
By using the grdclass attribute as Filter Attribute and by limiting the Filter
Min/Max values to the ground class (2), the module only uses ground
points for the triangulation. For the figure below, an analytical hillshade
was computed from the DTM (Terrain Analysis Lighting Analytical
Hillshading) and then placed on top of the DTM dataset with a
Transparency of 40%.
Tutorial ­ Ground Classification
Step 5: High resolution raster DTM generation

11. Multi Direction Lee Filter
Grid System 0.5; 1261x 1044y; . . .
Grid tut_class_TIN2Grid
Filtered Grid create
Estimated Noise (rel.) 1
Weighted
Method noise variance given
relative to mean
standard deviation
Usually, as most applications require a continuous surface, the DTM
created from the classified point cloud is finally smoothed. Here, we will
apply an edge preserving smoothing filter, the Grid Filter Multi
Direction Lee Filter module. Adjust the module settings as shown below
and execute the module. In order to get a better visual impression of the
filtering result, you can calculate another analytical hillshade from the
smoothed DTM dataset. The figure below shows an analytical hillshade
placed on top of the smoothed DTM dataset with a Transparency of
40%.
Tutorial ­ Ground Classification
Step 5: High resolution raster DTM generation