Description Usage Arguments Details Value Examples
Typical GIS operations modify gridded objects according to a given process. This can serve to identify certain objects or to prepare the quantitative assessment of the spatial object in question.
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input |
[ |
by |
[ |
sequential |
[ |
merge |
[ |
keepInput |
[ |
Operators can be called several successive times with modified arguments. The following operators are recently defined...
... to select a subset of cells:
rBounded
:
Select cells with values between an upper and lower threshold in a raster.
rGreater
: Select cells with values below a threshold in
a raster.
rLess
: Select cells with values above a
threshold in a raster.
rMask
: Select cells of a raster
based on a mask.
rMatch
: Match cells of a raster with a
kernel.
... to modify cell values:
rBinarise
:
Binarise the values in a raster.
rCategorise
: Assign
categories to the values in a raster.
rDistance
:
Calculate the distance map for a raster.
rFillNA
: Fill
NA values in a raster.
rOffset
: Offset the values in a
raster.
rPermute
: Assign a permutation to the cell
values of a raster.
rRange
: Change the scale of the
values in a raster.
rSubstitute
: Substitute values in a
raster.
... to determine objects:
rCentroid
:
Determine the centroid of foreground patches in a raster.
rPatches
: Determine foreground patches in a raster.
rSkeletonise
: Determine the skeleton of foreground patches in
a raster.
... to morphologically modify a raster:
rMorph
: Morphologically modify a raster.
rDilate
: Morphologically dilate foreground patches in a
raster.
rErode
: Morphologically erode foreground
patches in a raster.
... to modify the overall raster:
rBlend
:
Blend two rasters with each other.
rReduce
: Combine a
raster stack after segregation.
rRescale
: Rescale a
raster.
rSegregate
: Segregate values in a raster into
layers.
Moreover, you can create your own operator or check this package's github page to suggest new algorithms or make a pull-request.
A list of RasterLayer
s or a RasterStack
of modified
objects according to the number of chosen datasets and (combinations of)
operators.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | input <- rtRasters$continuous
# employ modification with merely one operator
binarised <- rBinarise(input, thresh = 40)
visualise(binarised)
# employ several operators combined to an algorithm, 'obj' does not need to
# be specified per operator in the algorithm, as 'modify' assigns it.
getPatches <- list(list(operator = "rBinarise", thresh = 40),
list(operator = "rPatches"))
patches <- modify(input, by = getPatches, sequential = TRUE)
visualise(patches)
# To run separated sub-algorithms, use names for each operator to specify
# which elements should be computed sequentially.
getPatchNCats <- list(get_patches = list(operator = "rBinarise", thresh = 40),
get_patches = list(operator = "rPatches"),
get_categories = list(operator = "rCategorise", n = 5))
patchNCats <- modify(input, by = getPatchNCats, merge = TRUE)
visualise(patchNCats)
# Create objects that are usable later in the algorithm
getMedialAxis <- list(skeleton = list(operator = "rSkeletonise", background = 0),
medAxis = list(operator = "rPermute"),
medAxis = list(operator = "rDistance"),
medAxis = list(operator = "rMask", mask = "skeleton"))
MAT <- modify(binarised, by = getMedialAxis, merge = TRUE)
visualise(MAT, trace = TRUE)
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