This function allows to classify satellite images using k-means.
rkmeans( img, k, iter.max = 100, nstart = 50, algo = c("Hartigan-Wong", "Lloyd", "Forgy", "MacQueen"), verbose = FALSE, ... )
| 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 |
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.
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. (2013). An introduction to statistical learning : with applications in R. New York: Springer.
library(ForesToolboxRS) # Load the dataset data(img_l8) # Select the best embedded algorithm in kmeans classKmeans <- rkmeans(img = img_l8, k = 4, algo = "MacQueen")