sits (version 0.13.1)

sits_label_majority: Post-process a classified data raster with a majority filter

Description

Takes a set of classified raster layers with labels, and executes a majority filter for post-processing.

Usage

sits_label_majority(cube, window_size = 3, output_dir = "./", version = "v1")

Arguments

cube

Classified image data cube.

window_size

Size of the window to apply majority filter

output_dir

Output directory where to out the file

version

Version of resulting image (in the case of multiple tests)

Value

A data cube with metadata information on post-processed data

Examples

# NOT RUN {
# Retrieve the samples for Mato Grosso
# select band "ndvi"

samples_ndvi <- sits_select(samples_modis_4bands, bands = "NDVI")

# select a random forest model
rfor_model <- sits_train(samples_ndvi, sits_rfor(num_trees = 500))

# Classify a raster file with 23 instances for one year
data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
# create a data cube based on the information about the files
cube <- sits_cube(
    source = "LOCAL",
    name = "sinop-2014",
    satellite = "TERRA",
    sensor = "MODIS",
    data_dir = data_dir,
    delim = "_",
    parse_info = c("X1", "X2", "tile", "band", "date")
)

# classify the raster image
probs_cube <- sits_classify(cube,
    ml_model = rfor_model,
    output_dir = tempdir(),
    memsize = 4, multicores = 2
)

# label the classification
label_cube <- sits_label_classification(probs_cube, output_dir = tempdir())

# smooth the result with a majority filter
label_maj_cube <- sits_label_majority(label_cube, output_dir = tempdir())

# }