predict.unsuperClass: Predict a raster map based on a unsuperClass model fit.

In bleutner/RStoolbox: Remote Sensing Data Analysis

Predict a raster map based on a unsuperClass model fit.

Description

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 class '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)

bleutner/RStoolbox documentation built on April 23, 2024, 9:36 a.m.