sits (version 1.4.1)

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

Value

A classified image cube.

Arguments

cube

Classified image cube to be reclassified.

mask

Classified image cube with additional information to be used in expressions.

rules

Named expressions to be evaluated (see details).

memsize

Memory available for classification (in GB).

multicores

Number of cores to be used for classification.

output_dir

Directory where files will be saved.

version

Version of resulting image (in the case of multiple runs).

Author

Rolf Simoes,

Gilberto Camara,

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.

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",
        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"),
        version = "v20220606"
    )

    # 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_Burn", "2" = "ClearCut_Soil",
                   "3" = "ClearCut_Veg",  "4" = "Forest")
    )

    # Reclassify cube
    ro_mask <- sits_reclassify(
        cube = ro_class,
        mask = prodes2021,
        rules = list(
            "Deforestation_Mask" = 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 == "Water",
            "NonForest" = mask %in% c("NonForest", "NonForest2")
        ),
        memsize = 4,
        multicores = 2,
        output_dir = tempdir(),
        version = "v2"
    )

    plot(ro_mask)
}