Tutorial ground classification with Laserdata LiS

TUTORIAL

Ground Classification

Version 1.0, January 2014Author: Volker Wichmann

This tutorial describes how to perform ground filtering of ALS pointclouds with LiS. LiS supports various filtering methods to classify groundpoints and to derive high resolution DTMs from this classification. Herewe will focus on a new hybrid approach, which combines progressiveTIN densification and segmentationbased filtering.

The tutorial includes the following topics: import of the ALS point cloudfrom LAZ, removal of outliers (low and air points), point cloudsegmentation and ground filtering. Finally, we will generate a highresolution raster DTM from the classification result.

Tutorial Ground Classification

Introduction

Import LAS/LAZ Files

Input Files c:\data\tut_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 LASformat. We will use the Import LAS/LAZ Files module to import thedata. Here, we will only import a selection of the available attributes. Incase 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, theimported dataset will show up in the Data tab of the Manager window. Bydoubleclicking on the dataset, you can visualize it in a Map View. As youcan see from the colorization of the dataset, there seem to exit someoutliers in the data. Do a rightclick on the dataset in the Data tab andselect Classification > Set Range to Standard Deviation (2.0) in order todiscard the outliers while applying the color palette.


Step 1: Import of ALS data from LAZ

Point Cloud Editor [interactive]

Point Cloud tut_class

The colorization of the dataset has already indicated that the datacontains some outliers. We will verify this by visualizing the dataset withthe LASERDATA LIS > Data Editing > Point Cloud > Point Cloud Editor[interactive] module. Adjust the settings as shown below and executethe module.You can use the F5 / F6 keys to scale the point size. It is obvious thatthere 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.


Step 2: Outlier removal

Remove Low and Air Points (PC)


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, whichfor example only remove isolated points, will fail to remove these pointclusters. For this reason we will use the LASERDATA LIS > DataAnalysis > Filtering > Point Cloud > Remove Low and Air Points (PC)module. Adjust the module settings as shown below and execute themodule.

With these settings both low and air points, as well as point clusters withup to 8 points. The computations will be done three times in order toiteratively remove outliers which approximately share the same x/zlocation but different elevation levels.The computations remove 1720 points from the dataset, which is lessthan 0.1% of the points.


Step 2: Outlier removal

Segmentation by Planes (PC)


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 progressiveTIN densification and point cloud segmentation, we will next use theLASERDATA LIS > Data Analysis > Segmentation > Segmentation byPlanes (PC) module. Adjust the module settings as shown below andexecute 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 finalsegments. By default, each point will get assigned a segment ID. Pointsfor which no plane could be fitted will get an ID of 1 and points whichwere skipped during region growing will get an ID of 0. That is, validsegments will have segment IDs greater than 0. Additionally, we willoutput the segment size (number of points per segment).


Step 3: Point cloud segmentation



Random segmentid

After module execution, the point cloud is colored by random using thesegment ID as color attribute. Change to the LASERDATA LIS > DataEditing > Point Cloud > Point Cloud Editor [interactive] module and dosome additional settings as shown below. Then execute the module.By providing the segmentid attribute with the Random parameter, we areable to use the keyboard shortcut q to automatically color the point cloudsegments by random. Each time you press the q key, a new random

palette will be assigned. Please note, that the keyboard shortcuts areonly 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 pointdensities some segments are formed even in vegetation.


Step 3: Point cloud segmentation

Ground Classification (PC)


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 allground points. Change to the LASERDATA LIS > Data Analysis >Classification > Ground Classification (PC) module. Adjust the modulesettings as shown below and execute the module.With these settings the seed generation will start with blocks of 50medge length (about the largest building size), and will end as soon as theblock size drops below 10m. Because of the steep topography in parts of

the area under investigation, the TIN densification parameters MaximumAngle and Maximum Distance have to be set to quite large values. Afterthe TIN has been constructed, all points with an elevation differencebelow the Z Tolerance get labelled as ground. Finally, all segments thatcontain a ground point and that have more than 500 points are alsolabelled as ground.


Step 4: Ground classification



Classification grdclass

Random segmentid

In order to validate the classification result, we have a more detailed lookwith the LASERDATA LIS > Data Editing > Point Cloud > Point CloudEditor [interactive] module. Provide the classification result with theClassification parameter and execute the module.By providing the grdclass attribute with the Classification parameter, weare able to use the keyboard shortcut 5 to automatically color the pointcloud segments by classification. This colors the ground points in brown

and all other points in grey. With the keyboard shortcut 6, only theground points are visualized.As you can see, the ground classification is quite complete, even in verysteep terrain. Also road and river embankments are labelled as groundcompletely. This is archived by combining the progressive TINdensification with the segmentation approach.


Step 4: Ground classification

Point Cloud to Grid (TIN)


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 theclassification result. We will use the LASERDATA LIS > Data Analysis >Conversion > Point Cloud > Point Cloud to Grid (TIN) module for thispurpose. Adjust the module settings as shown below and execute themodule. 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 groundpoints for the triangulation. For the figure below, an analytical hillshadewas computed from the DTM (Terrain Analysis > Lighting > AnalyticalHillshading) and then placed on top of the DTM dataset with aTransparency of 40%.


Step 5: High resolution raster DTM generation

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 DTMcreated from the classified point cloud is finally smoothed. Here, we willapply an edge preserving smoothing filter, the Grid > Filter > MultiDirection Lee Filter module. Adjust the module settings as shown belowand execute the module. In order to get a better visual impression of thefiltering result, you can calculate another analytical hillshade from thesmoothed DTM dataset. The figure below shows an analytical hillshade

placed on top of the smoothed DTM dataset with a Transparency of40%.


Step 5: High resolution raster DTM generation

Tutorial ground classification with Laserdata LiS

Technology

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