sits_reclassify: Reclassify a classified cube

In sits: Satellite Image Time Series Analysis for Earth Observation Data Cubes

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"
)

## S3 method for class 'tbl_df'
sits_reclassify(
  cube,
  mask,
  rules,
  memsize,
  multicores,
  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"
)
}

sits documentation built on Nov. 2, 2023, 5:59 p.m.