k_means function

k_means

Compute k-means clusters for a SpatRaster. For large SpatRasters (with ncell(x) > maxcell) this is done in two steps. First a sample of the cells is used to compute the cluster centers. Then each cell is assigned to a cluster by computing the distance to these centers.

## S4 method for signature 'SpatRaster'
k_means(x, centers=3, ..., maxcell=1000000, filename="", overwrite=FALSE, wopt=list())

Arguments

• x: SpatRaster
• centers: either the number of clusters, or a set of initial (distinct) cluster centres. If a number, a random set of (distinct) cells in x is chosen as the initial centres
• ...: additional arguments passed to kmeans
• maxcell: positive integer. The size of the regular sample used if it is smaller than ncell(x)
• filename: character. Output filename (ignored if as.raster=FALSE)
• overwrite: logical. If TRUE, filename is overwritten
• wopt: list with additional arguments for writing files as in writeRaster

Returns

SpatRaster

See Also

kmeans

Examples

f <- system.file("ex/logo.tif", package = "terra")
r <- rast(f)
km <- k_means(r, centers=5)
km