Build a labelled image from a probability cube — sits_label

Takes a set of classified raster layers with probabilities, and labels them based on the maximum probability for each pixel. This function is the final step of main the land classification workflow.

Usage

sits_label_classification(cube, ...)

# S3 method for class 'probs_cube'
sits_label_classification(
  cube,
  ...,
  memsize = 4L,
  multicores = 2L,
  output_dir,
  version = "v1",
  progress = TRUE
)

# S3 method for class 'probs_vector_cube'
sits_label_classification(
  cube,
  ...,
  output_dir,
  version = "v1",
  progress = TRUE
)

# S3 method for class 'raster_cube'
sits_label_classification(cube, ...)

# S3 method for class 'derived_cube'
sits_label_classification(cube, ...)

# Default S3 method
sits_label_classification(cube, ...)

Arguments

cube: Classified image data cube.
...: Other parameters for specific functions.
memsize: maximum overall memory (in GB) to label the classification.
multicores: Number of workers to label the classification in parallel.
output_dir: Output directory for classified files.
version: Version of resulting image (in the case of multiple runs).
progress: Show progress bar?

Value

A data cube with an image with the classified map.

Note

The main sits classification workflow has the following steps:

sits_cube: selects a ARD image collection from a cloud provider.
sits_cube_copy: copies an ARD image collection from a cloud provider to a local directory for faster processing.
sits_regularize: create a regular data cube from an ARD image collection.
sits_apply: create new indices by combining bands of a regular data cube (optional).
sits_get_data: extract time series from a regular data cube based on user-provided labelled samples.
sits_train: train a machine learning model based on image time series.
sits_classify: classify a data cube using a machine learning model and obtain a probability cube.
sits_smooth: post-process a probability cube using a spatial smoother to remove outliers and increase spatial consistency.
sits_label_classification: produce a classified map by selecting the label with the highest probability from a smoothed cube.

Please refer to the sits documentation available in https://e-sensing.github.io/sitsbook/ for detailed examples.

Author

Rolf Simoes, rolfsimoes@gmail.com

Felipe Souza, felipe.souza@inpe.br

Examples

if (sits_run_examples()) {
    # create a random forest model
    rfor_model <- sits_train(samples_modis_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 = "MOD13Q1-6.1",
        data_dir = data_dir
    )
    # classify a data cube
    probs_cube <- sits_classify(
        data = cube, ml_model = rfor_model, output_dir = tempdir()
    )
    # plot the probability cube
    plot(probs_cube)
    # smooth the probability cube using Bayesian statistics
    bayes_cube <- sits_smooth(probs_cube, output_dir = tempdir())
    # plot the smoothed cube
    plot(bayes_cube)
    # label the probability cube
    label_cube <- sits_label_classification(
        bayes_cube,
        output_dir = tempdir()
    )
    # plot the labelled cube
    plot(label_cube)
}