Takes a class cube with different labels and a sampling design with a number of samples per class and allocates a set of locations for each class
Usage
sits_stratified_sampling(
cube,
sampling_design,
alloc = "alloc_prop",
overhead = 1.2,
multicores = 2L,
memsize = 2L,
shp_file = NULL,
progress = TRUE
)Arguments
- cube
Classified cube
- sampling_design
Result of sits_sampling_design
- alloc
Allocation method chosen
- overhead
Additional percentage to account for border points
- multicores
Number of cores that will be used to sample the images in parallel.
- memsize
Memory available for sampling.
- shp_file
Name of shapefile to be saved (optional)
- progress
Show progress bar? Default is TRUE.
Author
Gilberto Camara, gilberto.camara@inpe.br
Felipe Carlos, efelipecarlos@gmail.com
Felipe Carvalho, felipe.carvalho@inpe.br
Examples
if (sits_run_examples()) {
# create a random forest model
rfor_model <- sits_train(samples_modis_ndvi, sits_rfor())
# create a data cube from local files
data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
cube <- sits_cube(
source = "BDC",
collection = "MOD13Q1-6.1",
data_dir = data_dir
)
# classify a data cube
probs_cube <- sits_classify(
data = cube, ml_model = rfor_model, output_dir = tempdir()
)
# label the probability cube
label_cube <- sits_label_classification(
probs_cube,
output_dir = tempdir()
)
# estimated UA for classes
expected_ua <- c(
Cerrado = 0.95, Forest = 0.95,
Pasture = 0.95, Soy_Corn = 0.95
)
# design sampling
sampling_design <- sits_sampling_design(label_cube, expected_ua)
# select samples
samples <- sits_stratified_sampling(
label_cube,
sampling_design, "alloc_prop"
)
}