filters.elm

The Extended Local Minimum (ELM) filter marks low points as noise. This filter is an implementation of the method described in [Chen2012].

ELM begins by rasterizing the input point cloud data at the given cell size. Within each cell, the lowest point is considered noise if the next lowest point is a given threshold above the current point. If it is marked as noise, the difference between the next two points is also considered, marking points as noise if needed, and continuing until another neighbor is found to be within the threshold. At this point, iteration for the current cell stops, and the next cell is considered.

Example #1

The following PDAL pipeline applies the ELM filter, using a cell size of 20 and applying the classification code of 18 to those points determined to be noise.

{
  "pipeline":[
    "input.las",
    {
      "type":"filters.elm",
      "cell":20.0,
      "class":18
    },
    "output.las"
  ]
}

Example #2

This variation of the pipeline begins by assigning a value of 0 to all classifications, thus resetting any existing classifications. It then proceeds to compute ELM with a threshold value of 2.0, and finishes by extracting all returns that are not marked as noise.

{
  "pipeline":[
    "input.las",
    {
      "type":"filters.assign",
      "assignment":"Classification[:]=0"
    },
    {
      "type":"filters.elm",
      "threshold":2.0
    },
    {
      "type":"filters.range",
      "limits":"Classification![7:7]"
    },
    "output.las"
  ]
}

Options

cell
Cell size. [Default: 10.0]
class
Classification value to apply to noise points. [Default: 7]
threshold
Threshold value to identify low noise points. [Default: 1.0]