Identifying ground
This exercise uses PDAL to classify ground returns using the Simple Morphological Filter (SMRF) technique.
Note
This exercise is an adaptation of the pcl_ground tutorial on the PDAL website by Brad Chambers. You can find more detail and example invocations there.
Exercise
The primary input for Digital Terrain Model generation is a point cloud with ground vs. not-ground classifications. In this example, we will use an algorithm provided by PDAL, the Simple Morphological Filter technique to generate a ground surface.
See also
You can read more about the specifics of the SMRF algorithm from [Pingle2013]_
Command
Invoke the following command, substituting accordingly, in your Conda Shell:
1pdal translate ./exercises/analysis/ground/CSite1_orig-utm.laz \
2-o ./exercises/analysis/ground/ground.laz \
3smrf \
4-v 4
1pdal translate ./exercises/analysis/ground/CSite1_orig-utm.laz ^
2-o ./exercises/analysis/ground/ground.laz ^
3smrf ^
4-v 4
As we can see, the algorithm does a great job of discriminating the points, but there’s a few issues.
There’s noise underneath the main surface that will cause us trouble when we generate a terrain surface.
Filtering
We do not yet have a satisfactory surface for generating a DTM. When we visualize the output of this ground operation, we notice there’s still some noise. We can stack the call to SMRF with a call to a the filters.outlier technique we learned about in denoising.
Let us start by removing the non-ground data to just view the ground data:
1pdal translate \
2./exercises/analysis/ground/CSite1_orig-utm.laz \
3-o ./exercises/analysis/ground/ground.laz \
4smrf range \
5--filters.range.limits="Classification[2:2]" \
6-v 4
1pdal translate ^
2./exercises/analysis/ground/CSite1_orig-utm.laz ^
3-o ./exercises/analysis/ground/ground.laz ^
4smrf range ^
5--filters.range.limits="Classification[2:2]" ^
6-v 4
2. Now we will instead use the translate command to stack the filters.outlier and filters.smrf stages:
1pdal translate ./exercises/analysis/ground/CSite1_orig-utm.laz \
2-o ./exercises/analysis/ground/denoised-ground-only.laz \
3outlier smrf range \
4--filters.outlier.method="statistical" \
5--filters.outlier.mean_k=8 --filters.outlier.multiplier=3.0 \
6--filters.smrf.ignore="Classification[7:7]" \
7--filters.range.limits="Classification[2:2]" \
8--writers.las.compression=true \
9--verbose 4
1pdal translate ./exercises/analysis/ground/CSite1_orig-utm.laz ^
2-o ./exercises/analysis/ground/denoised-ground-only.laz ^
3outlier smrf range ^
4--filters.outlier.method="statistical" ^
5--filters.outlier.mean_k=8 --filters.outlier.multiplier=3.0 ^
6--filters.smrf.ignore="Classification[7:7]" ^
7--filters.range.limits="Classification[2:2]" ^
8--writers.las.compression=true ^
9--verbose 4
In this invocation, we have more control over the process. First the outlier
filter merely classifies outliers with a Classification value of 7. These
outliers are then ignored during SMRF processing with the ignore option.
Finally, we add a range filter to extract only the ground returns (i.e.,
Classification value of 2).
The result is a more accurate representation of the ground returns.
