| decimate_points {lidR} | R Documentation |
Reduce the number of points using several possible algorithms.
decimate_points(las, algorithm)
las |
An object of class LAS or LAScatalog. |
algorithm |
function. An algorithm of point decimation. |
If the input is a LAS object, returns a LAS object. If the input is a
LAScatalog, returns a LAScatalog.
LAScatalogThis section appears in each function that supports a LAScatalog as input.
In lidR when the input of a function is a LAScatalog the
function uses the LAScatalog processing engine. The user can modify the engine options using
the available options. A careful reading of the
engine documentation is recommended before processing LAScatalogs. Each
lidR function should come with a section that documents the supported engine options.
The LAScatalog engine supports .lax files that significantly improve the computation
speed of spatial queries using a spatial index. Users should really take advantage a .lax files,
but this is not mandatory.
Supported processing options for a LAScatalog (in bold). For more details see the
LAScatalog engine documentation:
chunk size: How much data is loaded at once.
chunk buffer: This function guarantee a strict wall-to-wall continuous output. The buffer
option is not considered.
chunk alignment: Align the processed chunks.
progress: Displays a progression estimation.
output files*: Mandatory because the output is likely to be too big to be returned
in R and needs to be written in las/laz files. Supported templates are {XLEFT}, {XRIGHT},
{YBOTTOM}, {YTOP}, {XCENTER}, {YCENTER} {ID} and, if
chunk size is equal to 0 (processing by file), {ORIGINALFILENAME}.
select: The function will write files equivalent to the original ones. Thus select = "*"
and cannot be changed.
filter: Read only points of interest.
LASfile <- system.file("extdata", "Megaplot.laz", package="lidR")
las = readLAS(LASfile, select = "xyz")
# Select points randomly to reach an overall density of 1
thinned1 = decimate_points(las, random(1))
#plot(grid_density(las))
#plot(grid_density(thinned1))
# Select points randomly to reach an homogeneous density of 1
thinned2 = decimate_points(las, homogenize(1,5))
#plot(grid_density(thinned2))
# Select the highest point within each pixel of an overlayed grid
thinned3 = decimate_points(las, highest(5))
#plot(thinned3)