sits_label_classification: Build a labelled image from a probability cube

In e-sensing/sits: Satellite Image Time Series Analysis for Earth Observation Data Cubes

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, ...)

## 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

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

Author(s)

Rolf Simoes, [e-mail]

Felipe Souza, [e-mail]

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)
}

e-sensing/sits documentation built on Feb. 13, 2025, 2:22 a.m.