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, ...)
# S3 method for class 'class_cube'
sits_reclassify(
cube,
...,
mask,
rules,
memsize = 4L,
multicores = 2L,
output_dir,
version = "v1",
progress = TRUE
)
# Default S3 method
sits_reclassify(cube, ...)Arguments
- cube
Image cube to be reclassified (class = "class_cube")
- ...
Other parameters for specific functions.
- 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).
- progress
Set progress bar??
Note
Reclassification of a remote sensing map refers to changing the classes assigned to different pixels in the image. Reclassification involves assigning new classes to pixels based on additional information from a reference map. Users define rules according to the desired outcome. These rules are then applied to the classified map to produce a new map with updated classes.
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",
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"
)
}