.. _filters.pmf:

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
[Zhang2003]_.

.. embed::

Example
-------

.. code-block:: json

    {
      "pipeline":[
        "input.las",
        {
          "type":"filters.pmf"
        },
        {
          "type":"writers.las",
          "filename":"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 :ref:`range filter<filters.range>` to the pipeline.

.. code-block:: json

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

.. note::

    [Zhang2003]_ describes the consequences and relationships of the parameters
    in more detail and is the canonnical resource on the topic.

Options
-------------------------------------------------------------------------------

cell_size
  Cell Size. [Default: **1**]

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

ignore
  Optional range of values to ignore.
  
initial_distance
  Initial distance. [Default: **0.15**]

last
  Consider only last returns (when return information is available)? [Default:
  **true**]
  
max_distance
  Maximum distance. [Default: **2.5**]

max_window_size
  Maximum window size. [Default: **33**]

slope
  Slope. [Default: **1.0**]
