sits (version 1.1.0)

sits_label_classification: Build a labelled image from a probability cube

Description

Takes a set of classified raster layers with probabilities, and label them based on the maximum probability for each pixel.

Usage

sits_label_classification(
  cube,
  multicores = 2,
  memsize = 4,
  output_dir = ".",
  version = "v1"
)

Value

A data cube with an image with the classified map.

Arguments

cube

Classified image data cube.

multicores

Number of workers to label the classification in parallel.

memsize

maximum overall memory (in GB) to label the classification.

output_dir

Output directory for classified files.

version

Version of resulting image (in the case of multiple runs).

Author

Rolf Simoes,

Examples

if (sits_run_examples()) {
    # select a set of samples
    samples_ndvi <- sits_select(samples_modis_4bands, bands = c("NDVI"))
    # create a random forest model
    rfor_model <- sits_train(samples_ndvi, sits_rfor())
    # create a data cube from local files
    data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
    cube <- sits_cube(
        source = "BDC",
        collection = "MOD13Q1-6",
        data_dir = data_dir,
        delim = "_",
        parse_info = c("X1", "X2", "tile", "band", "date")
    )
    # classify a data cube
    probs_cube <- sits_classify(data = cube, ml_model = rfor_model)
    # plot the probability cube
    plot(probs_cube)
    # smooth the probability cube using Bayesian statistics
    bayes_cube <- sits_smooth(probs_cube)
    # plot the smoothed cube
    plot(bayes_cube)
    # label the probability cube
    label_cube <- sits_label_classification(bayes_cube)
    # plot the labelled cube
    plot(label_cube)
}