rasclass (version 0.1.0)

classifyMLC: Maximum Likelihood Classifier

Description

A function to classify raster data using the Maximum Likelihood Classifier.

Usage

classifyMLC(object, splitfraction = 1)

Arguments

object

A rasclass object containing the data and a built formula.

splitfraction

An optional numeric argument specifying a fraction to split data.

Value

• A rasclass-class object, containing the classified raster in the predictedGrid slot.

Details

Data is classified using the Maximum Likelihood Classifier and results are stored in the predictedGrid slot. The formula to classify the data is taken from the call slot of the input rasclass object.

The optional argument splitfraction allows splitting the data randomly into two parts with the fraction specified. The classification model will then be trained on one fraction and the other fraction will be used for prediction. This allows testing the preformance of the classification on data that was not an input for training the model.

References

Paola, J. D., Schowengerdt, R. A. (1995). A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification. IEEE Transactions on Geoscience and Remote Sensing, 33(4), 981-996.

See Also

accuracyAssessment, buildFormula, checkRasclass, classifyMLC, classifyMlogit, rasclass-class, rasclassRaster-class, readRaster, readRasterFolder, setRasclassData, writeRaster

Examples

# Load data from external folder
object <- readRasterFolder(path = "mypath", samplename = "mysample",
	filenames = c('myvar1.asc', 'myvar2.asc'))

# For this example, get data from a random dataframe
mysample <- sample(c(NA, 1, 2, 3), 20000, rep = TRUE)
red <- sample(c(NA,1:255), 20000, rep = TRUE)
green <- sample(c(NA,1:255), 20000, rep = TRUE)
blue <- sample(c(NA,1:255), 20000, rep = TRUE)
newdata <- data.frame(mysample,red,green,blue)

# Prepare object using the dataframe and specifying raster properties
object <- new('rasclass')
object <- setRasclassData(newdata, object, ncols = 100, nrows = 200,
	xllcorner = 0, yllcorner = 0, cellsize = 10, NAvalue = -9999,
	samplename = 'mysample')

# Classify and compute accuracy
object <- classifyMLC(object)
object <- accuracyAssessment(object)

# Summarize and plot results
summary(object)
image(object@predictedGrid)