filters.pmf

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filters.pmf#

The Progressive Morphological Filter (PMF) is a method of segmenting ground and non-ground returns. This filter is an implementation of the method described in [Zhang et al., 2003].

Default Embedded Stage

This stage is enabled by default

Example#

[
    "input.las",
    {
        "type":"filters.pmf"
    },
    "output.las"
]

Notes#

  • [slope] controls the height threshold at each iteration. A slope of 1.0 represents a 1:1 or 45º.

  • [initial_distance] is _intended_ to be set to account for z noise, so for a flat surface if you have an uncertainty of around 15 cm, you set [initial_distance] large enough to not exclude these points from the ground.

  • For a given iteration, the height threshold is determined by multiplying slope by [cell_size] by the difference in window size between the current and last iteration, plus the [initial_distance]. This height threshold is constant across all cells and is maxed out at the [max_distance] value. If the difference in elevation between a point and its “opened” value (from the morphological operator) exceeds the height threshold, it is treated as non-ground. So, bigger slope leads to bigger height thresholds, and these grow with each iteration (not to exceed the max). With flat terrain, keep this low, the thresholds are small, and stuff is more aggressively dumped into non-ground class. In rugged terrain, open things up a little, but then you can start missing buildings, veg, etc.

  • Very large [max_window_size] values will result in a lot of potentially extra iteration. This parameter can have a strongly negative impact on computation performance.

  • [exponential] is used to control the rate of growth of morphological window sizes toward [max_window_size]. Linear growth preserves gradually changing topographic features well, but demands considerable compute time. The default behavior is to grow the window sizes exponentially, thus reducing the number of iterations.

  • This filter will mark all returns deemed to be ground returns with a classification value of 2 (per the LAS specification). To extract only these returns, users can add a range filter to the pipeline.

{
  "type":"filters.range",
  "limits":"Classification[2:2]"
}

Note

[Zhang et al., 2003] describes the consequences and relationships of the parameters in more detail and is the canonical resource on the topic.

Options#

cell_size

Cell Size. [Default: 1]

exponential

Use exponential growth for window sizes? [Default: true]

ignore

Range of values to ignore. [Optional]

initial_distance

Initial distance. [Default: 0.15]

returns

Comma-separated list of return types into which data should be segmented. Valid groups are “last”, “first”, “intermediate” and “only”. [Default: “last, only”]

max_distance

Maximum distance. [Default: 2.5]

max_window_size

Maximum window size. [Default: 33]

slope

Slope. [Default: 1.0]

where

An expression that limits points passed to a filter. Points that don’t pass the expression skip the stage but are available to subsequent stages in a pipeline. [Default: no filtering]

where_merge

A strategy for merging points skipped by a where option when running in standard mode. If true, the skipped points are added to the first point view returned by the skipped filter. If false, skipped points are placed in their own point view. If auto, skipped points are merged into the returned point view provided that only one point view is returned and it has the same point count as it did when the filter was run. [Default: auto]

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