Normalize intensity
Normalize intensity values using multiple methods.
normalize_intensity(las, algorithm)
las |
An object of class LAS or LAScatalog. |
algorithm |
an intensity normalizaton algorithm. |
Returns an object of class LAS. The attribute 'Intensity' records the normalized intensity. An extra attribute named 'RawIntensity' records the original intensities.
LAScatalog
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 for a LAScatalog (in bold). For more details see the
LAScatalog engine documentation:
chunk size: How much data is loaded at once.
chunk buffer: No buffer needed. A buffer of 0 is used and cannot be changed
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.
Other normalize:
normalize_height()
# A valid file properly populated
LASfile <- system.file("extdata", "Topography.laz", package="lidR")
las <- readLAS(LASfile)
# pmin = 15 because it is an extremely small file
# strongly decimated to reduce its size. There are
# actually few multiple returns
sensor <- track_sensor(las, Roussel2020(pmin = 15))
# Here the effect is virtually null because the size of
# the sample is too small to notice any effect of range
las <- normalize_intensity(las, range_correction(sensor, Rs = 2000))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.