R: Smooth probability cubes with spatial predictors

sits_smooth {sits}

R Documentation

Smooth probability cubes with spatial predictors

Description

Takes a set of classified raster layers with probabilities, whose metadata is]created by sits_cube, and applies a smoothing function. There are three options, defined by the "type" parameter:

"bayes": Use a bayesian smoother
"gaussian": Use a gaussian smoother
"bilateral: Use a bilateral smoother

Usage

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

## S3 method for class 'bayes'
sits_smooth(
  cube,
  type = "bayes",
  ...,
  window_size = 5,
  smoothness = 20,
  covar = FALSE,
  multicores = 2,
  memsize = 4,
  output_dir = ".",
  version = "v1"
)

## S3 method for class 'gaussian'
sits_smooth(
  cube,
  type = "gaussian",
  ...,
  window_size = 5,
  sigma = 1,
  multicores = 2,
  memsize = 4,
  output_dir = ".",
  version = "v1"
)

## S3 method for class 'bilateral'
sits_smooth(
  cube,
  type = "bilateral",
  ...,
  window_size = 5,
  sigma = 8,
  tau = 0.1,
  multicores = 2,
  memsize = 4,
  output_dir = ".",
  version = "v1"
)

Arguments

`cube`	Probability data cube
`type`	Type of smoothing
`...`	Parameters for specific functions
`window_size`	Size of the neighbourhood.
`smoothness`	Estimated variance of logit of class probabilities (Bayesian smoothing parameter). It can be either a matrix or a scalar.
`covar`	a logical argument indicating if a covariance matrix must be computed as the prior covariance for bayesian smoothing.
`multicores`	Number of cores to run the smoothing function
`memsize`	Maximum overall memory (in GB) to run the smoothing.
`output_dir`	Output directory for image files
`version`	Version of resulting image (in the case of multiple tests)
`sigma`	Standard deviation of the spatial Gaussian kernel (for gaussian and bilateral smoothing)
`tau`	Standard deviation of the class probs value (for bilateral smoothing)

Value

A tibble with metadata about the output raster objects.

Author(s)

Gilberto Camara, gilberto.camara@inpe.br

Rolf Simoes, rolf.simoes@inpe.br

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 (for gaussian and bilateral smoothing)

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))

# create a data cube based on the information about the files
data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
cube <- sits_cube(
    source = "BDC",
    collection = "MOD13Q1-6",
    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
)

# smooth the result with a bayesian filter
bayes_cube <- sits_smooth(probs_cube,
     type = "bayes", output_dir = tempdir()
)

# smooth the result with a gaussian filter
gauss_cube <- sits_smooth(probs_cube,
    type = "gaussian", output_dir = tempdir()
)

# smooth the result with a bilateral filter
bil_cube <- sits_smooth(probs_cube,
    type = "bilateral", output_dir = tempdir()
)

## End(Not run)

[Package sits version 0.16.2 Index]