This exercise uses PDAL to classify ground returns using the Simple Morphological Filter (SMRF) technique.
This excerise is an adaptation of the Identifying ground returns using ProgressiveMorphologicalFilter segmentation tutorial on the PDAL website by Brad Chambers. You can find more detail and example invocations there.
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.
You can read more about the specifics of the SMRF algorithm from [Pingle2013]_`
Invoke the following command, substituting accordingly, in your Anaconda Shell:
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pdal translate ^ c:/Users/hobu/PDAL/exercises/analysis/ground/CSite1_orig-utm.laz ^ -o c:/Users/hobu/PDAL/exercises/analysis/ground/ground.laz ^ smrf ^ -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.
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 PMF with a call to a the filters.outlier technique we learned about in Removing noise.
- Let us start by removing the non-ground data:
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pdal translate ^ c:/Users/hobu/PDAL/exercises/analysis/ground/CSite1_orig-utm.laz ^ -o c:/Users/hobu/PDAL/exercises/analysis/ground/ground.laz ^ smrf ^ range ^ --filters.range.limits="Classification[2:2]" ^ -v 4
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pdal translate ^ c:/Users/hobu/PDAL/exercises/analysis/ground/CSite1_orig-utm.laz ^ -o c:/Users/hobu/PDAL/exercises/analysis/ground/denoised-ground-only.laz ^ outlier smrf range ^ --filters.outlier.method="statistical" ^ --filters.outlier.mean_k=8 ^ --filters.outlier.multiplier=3.0 ^ --filters.smrf.ignore="Classification[7:7]" ^ --filters.range.limits="Classification[2:2]" ^ --writers.las.compression=true --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 PMF processing with the
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.