sits (version 0.10.0)

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

Description

Takes a set of classified raster layers with probabilities, whose metadata is]created by sits_cube, and apply gaussian smoothing process.

Usage

# S3 method for gaussian
sits_smooth(
  cube,
  type = "gaussian",
  ...,
  window_size = 5,
  sigma = 0.85,
  output_dir = "./",
  version = "v1"
)

Arguments

cube

Probability data cube

type

Type of smoothing

...

Parameters for specific functions

window_size

Size of the neighbourhood.

sigma

Standard deviation of the spatial gaussian kernel

output_dir

Output directory where to out the file

version

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

Value

A tibble with metadata about the output raster objects.

References

K. Schindler, "An Overview and Comparison of Smooth Labeling Methods for Land-Cover Classification", IEEE Transactions on Geoscience and Remote Sensing, 50 (11), 4534-4545, 2012.

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
)

# smooth the result with a gaussian filter
sinop_gauss <- sits_smooth(sinop_probs,
    type = "gaussian",
    output_dir = tempdir()
    )
# }
# NOT RUN {
# }