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.
# S3 method for unsuperClass predict(object, img, output = "classes", ...)
Returns a raster with the K-means distances base on your object passed in the arguments.
unsuperClass object
Raster object. Layernames must correspond to layernames used to train the superClass model, i.e. layernames in the original raster image.
Character. Either 'classes' (kmeans class; default) or 'distances' (euclidean distance to each cluster center).
further arguments to be passed to writeRaster, e.g. filename
## Load training data ## Perform unsupervised classification uc <- unsuperClass(rlogo, nClasses = 10) ## Apply the model to another raster map <- predict(uc, rlogo)
Run the code above in your browser using DataLab