# 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.

1. 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.