Predict a raster map based on a unsuperClass model fit.

applies a kmeans cluster model to all pixels of a raster. Useful if you want to apply a kmeans model of scene A to scene B.

Usage

# S3 method for unsuperClass
predict(object, img, output = "classes", ...)

Arguments

object

unsuperClass object

img

Raster object. Layernames must correspond to layernames used to train the superClass model, i.e. layernames in the original raster image.

output

Character. Either 'classes' (kmeans class; default) or 'distances' (euclidean distance to each cluster center).

...

further arguments to be passed to writeRaster, e.g. filename

Value

Returns a raster with the K-means distances base on your object passed in the arguments.

Examples

## Load training data

## Perform unsupervised classification
uc  <- unsuperClass(rlogo, nClasses = 10)

## Apply the model to another raster
map <- predict(uc, rlogo)