sits_clean {sits}

Cleans a classified map using a local window

Description

Applies a modal function to clean up possible noisy pixels keeping the most frequently values within the neighborhood. In a tie, the first value of the vector is considered.

Usage

sits_clean(
  cube,
  window_size = 5L,
  memsize = 4L,
  multicores = 2L,
  output_dir,
  version = "v1-clean",
  progress = TRUE
)

## S3 method for class 'class_cube'
sits_clean(
  cube,
  window_size = 5L,
  memsize = 4L,
  multicores = 2L,
  output_dir,
  version = "v1-clean",
  progress = TRUE
)

## S3 method for class 'raster_cube'
sits_clean(
  cube,
  window_size = 5L,
  memsize = 4L,
  multicores = 2L,
  output_dir,
  version = "v1-clean",
  progress = TRUE
)

## S3 method for class 'derived_cube'
sits_clean(
  cube,
  window_size = 5L,
  memsize = 4L,
  multicores = 2L,
  output_dir,
  version = "v1-clean",
  progress = TRUE
)

## Default S3 method:
sits_clean(
  cube,
  window_size = 5L,
  memsize = 4L,
  multicores = 2L,
  output_dir,
  version = "v1-clean",
  progress = TRUE
)

Arguments

cube

Classified data cube (tibble of class "class_cube").

window_size

An odd integer representing the size of the sliding window of the modal function (min = 1, max = 15).

memsize

Memory available for classification in GB (integer, min = 1, max = 16384).

multicores

Number of cores to be used for classification (integer, min = 1, max = 2048).

output_dir

Valid directory for output file. (character vector of length 1).

version

Version of the output file (character vector of length 1)

progress

Logical: Show progress bar?

Value

A tibble with an classified map (class = "class_cube").

Author(s)

Felipe Carvalho, [e-mail]

Examples

if (sits_run_examples()) {
rf_model <- sits_train(samples_modis_ndvi, ml_method = 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
)
# classify a data cube
probs_cube <- sits_classify(
    data = cube,
    ml_model = rf_model,
    output_dir = tempdir()
)
# label the probability cube
label_cube <- sits_label_classification(
    probs_cube,
    output_dir = tempdir()
)
# apply a mode function in the labelled cube
clean_cube <- sits_clean(
    cube = label_cube,
    window_size = 5,
    output_dir = tempdir(),
    multicores = 1
)
}