decimate_points: Decimate a LAS object

Description

Reduce the number of points using several possible algorithms.

Usage

decimate_points(las, algorithm)

Arguments

las

An object of class LAS or LAScatalog.

algorithm

function. An algorithm of point decimation. lidR have: random, homogenize and highest.

Value

If the input is a LAS object, returns a LAS object. If the input is a LAScatalog, returns a LAScatalog.

Working with a <code>LAScatalog</code>

This 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

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.

Examples

Run this code

# NOT RUN {
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)
# }

Run the code above in your browser using DataCamp Workspace