sits (version 1.5.2)

sits_variance: Calculate the variance of a probability cube

Description

Takes a probability cube and estimate the local variance of the logit of the probability, to support the choice of parameters for Bayesian smoothing.

Usage

sits_variance(
  cube,
  window_size = 9L,
  neigh_fraction = 0.5,
  memsize = 4L,
  multicores = 2L,
  output_dir,
  version = "v1"
)
# S3 method for probs_cube
sits_variance(
  cube,
  window_size = 9L,
  neigh_fraction = 0.5,
  memsize = 4L,
  multicores = 2L,
  output_dir,
  version = "v1"
)
# S3 method for raster_cube
sits_variance(
  cube,
  window_size = 7L,
  neigh_fraction = 0.5,
  memsize = 4L,
  multicores = 2L,
  output_dir,
  version = "v1"
)
# S3 method for derived_cube
sits_variance(
  cube,
  window_size = 7L,
  neigh_fraction = 0.5,
  memsize = 4L,
  multicores = 2L,
  output_dir,
  version = "v1"
)
# S3 method for default
sits_variance(
  cube,
  window_size = 7L,
  neigh_fraction = 0.5,
  memsize = 4L,
  multicores = 2L,
  output_dir,
  version = "v1"
)

Value

A variance data cube.

Arguments

cube

Probability data cube (class "probs_cube")

window_size

Size of the neighborhood (odd integer)

neigh_fraction

Fraction of neighbors with highest probability for Bayesian inference (numeric from 0.0 to 1.0)

memsize

Maximum overall memory (in GB) to run the smoothing (integer, min = 1, max = 16384)

multicores

Number of cores to run the smoothing function (integer, min = 1, max = 2048)

output_dir

Output directory for image files (character vector of length 1)

version

Version of resulting image (character vector of length 1)

Author

Gilberto Camara,

Rolf Simoes,

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
    var_cube <- sits_variance(probs_cube, output_dir = tempdir())
    # plot the variance cube
    plot(var_cube)
}