rkmeans: Unsupervised classification - kmeans

In ytarazona/ForesToolboxRS: Remote Sensing Tools for Forest Monitoring

Unsupervised classification - kmeans

Description

This function allows to classify satellite images using k-means.

Usage

rkmeans(
  img,
  k,
  iter.max = 100,
  nstart = 50,
  algo = c("Hartigan-Wong", "Lloyd", "Forgy", "MacQueen"),
  verbose = FALSE,
  ...
)

Arguments

img	RasterStack or RasterBrick
k	the number of clusters
iter.max	The maximum number of iterations allowed
nstart	if centers is a number, how many random sets should be chosen?
algo	It can be "Hartigan-Wong", "Lloyd", "Forgy" or "MacQueen". See kmeans
verbose	This paramater is Logical. It Prints progress messages during execution.
...	Options to be passed to the function. See kmeans

Details

In principle, this function allows to classify satellite images specifying a k value, however it is recommended to find the optimal value of k using the calkmeans function.

If warnings such as "Quick-TRANSfer stage steps exceeded maximum" or "did not converge in 10 iterations" are obtained, it will be necessary to increase the iterations in 20 or 30 (i.e., inter.max = 20 or iter.max = 30). This issue is usually obtained with "Hartigan-Wong". See details of kmeans.

References

Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. (2013). An introduction to statistical learning : with applications in R. New York: Springer.

Examples

library(ForesToolboxRS)

# Load the dataset
data(img_l8)

# Select the best embedded algorithm in kmeans
classKmeans <- rkmeans(img = img_l8, k = 4, algo = "MacQueen")

ytarazona/ForesToolboxRS documentation built on Nov. 23, 2024, 6:03 p.m.