sits_reclassify {sits}

Reclassify a classified cube

Description

Apply a set of named expressions to reclassify a classified image. The expressions should use character values to refer to labels in logical expressions.

Usage

sits_reclassify(
  cube,
  mask,
  rules,
  memsize = 4L,
  multicores = 2L,
  output_dir,
  version = "v1"
)

## S3 method for class 'class_cube'
sits_reclassify(
  cube,
  mask,
  rules,
  memsize = 4L,
  multicores = 2L,
  output_dir,
  version = "v1"
)

## Default S3 method:
sits_reclassify(
  cube,
  mask,
  rules,
  memsize,
  multicores,
  output_dir,
  version = "v1"
)

Arguments

cube

Image cube to be reclassified (class = "class_cube")

mask

Image cube with additional information to be used in expressions (class = "class_cube").

rules

Expressions to be evaluated (named list).

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

Directory where files will be saved (character vector of length 1 with valid location).

version

Version of resulting image (character).

Details

sits_reclassify() allow any valid R expression to compute reclassification. User should refer to cube and mask to construct logical expressions. Users can use can use any R expression that evaluates to logical. TRUE values will be relabeled to expression name. Updates are done in asynchronous manner, that is, all expressions are evaluated using original classified values. Expressions are evaluated sequentially and resulting values are assigned to output cube. Last expressions has precedence over first ones.

Value

An object of class "class_cube" (reclassified cube).

Author(s)

Rolf Simoes, [e-mail]

Gilberto Camara, [e-mail]

Examples

if (sits_run_examples()) {
# Open mask map
data_dir <- system.file("extdata/raster/prodes", package = "sits")
prodes2021 <- sits_cube(
    source = "USGS",
    collection = "LANDSAT-C2L2-SR",
    data_dir = data_dir,
    parse_info = c(
        "X1", "X2", "tile", "start_date", "end_date",
        "band", "version"
    ),
    bands = "class",
    version = "v20220606",
    labels = c("1" = "Forest", "2" = "Water", "3" = "NonForest",
               "4" = "NonForest2", "6" = "d2007", "7" = "d2008",
               "8" = "d2009", "9" = "d2010", "10" = "d2011",
               "11" = "d2012", "12" = "d2013", "13" = "d2014",
               "14" = "d2015", "15" = "d2016", "16" = "d2017",
               "17" = "d2018", "18" = "r2010", "19" = "r2011",
               "20" = "r2012", "21" = "r2013", "22" = "r2014",
               "23" = "r2015", "24" = "r2016", "25" = "r2017",
               "26" = "r2018", "27" = "d2019", "28" = "r2019",
               "29" = "d2020", "31" = "r2020", "32" = "Clouds2021",
               "33" = "d2021", "34" = "r2021"),
    progress = FALSE
)
#' Open classification map
data_dir <- system.file("extdata/raster/classif", package = "sits")
ro_class <- sits_cube(
    source = "MPC",
    collection = "SENTINEL-2-L2A",
    data_dir = data_dir,
    parse_info = c(
        "X1", "X2", "tile", "start_date", "end_date",
        "band", "version"
    ),
    bands = "class",
    labels = c(
        "1" = "ClearCut_Fire", "2" = "ClearCut_Soil",
        "3" = "ClearCut_Veg", "4" = "Forest"
    ),
    progress = FALSE
)
# Reclassify cube
ro_mask <- sits_reclassify(
    cube = ro_class,
    mask = prodes2021,
    rules = list(
        "Old_Deforestation" = mask %in% c(
            "d2007", "d2008", "d2009",
            "d2010", "d2011", "d2012",
            "d2013", "d2014", "d2015",
            "d2016", "d2017", "d2018",
            "r2010", "r2011", "r2012",
            "r2013", "r2014", "r2015",
            "r2016", "r2017", "r2018",
            "d2019", "r2019", "d2020",
            "r2020", "r2021"
        ),
        "Water_Mask" = mask == "Water",
        "NonForest_Mask" = mask %in% c("NonForest", "NonForest2")
    ),
    memsize = 4,
    multicores = 2,
    output_dir = tempdir(),
    version = "ex_reclassify"
)
}