sits (version 0.10.0)

sits_smooth: Post-process a classified data raster probs using smoothing

Description

Takes a set of classified raster layers with probabilities, whose metadata is]created by sits_cube, and applies a smoothing function

Usage

sits_smooth(cube, type = "bayes", ...)

Arguments

cube

Probability data cube

type

Type of smoothing

...

Parameters for specific functions

Value

A tibble with metadata about the output raster objects.

Examples

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

samples_ndvi <- sits_select(samples_mt_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
files <- c(system.file("extdata/raster/mod13q1/sinop-crop-ndvi.tif",
    package = "sits"
))

# create a data cube based on the information about the files
sinop <- sits_cube(
    type = "BRICK", satellite = "TERRA",
    sensor = "MODIS", name = "Sinop-crop",
    timeline = timeline_modis_392,
    output_dir = tempdir(),
    bands = c("NDVI"), files = files
)

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

# label the classification and smooth the result with a bayesian filter
sinop_bayes <- sits_smooth(sinop_probs,
    output_dir = tempdir()
    )
# }
# NOT RUN {
# }