| sits_label_classification {sits} | R Documentation |
Takes a set of classified raster layers with probabilities, and label them based on the maximum probability for each pixel.
sits_label_classification( cube, multicores = 2, memsize = 4, output_dir = ".", version = "v1" )
cube |
Classified image data cube. |
multicores |
Number of process to label the classification in snow subprocess. |
memsize |
Maximum overall memory (in GB) to label the classification. |
output_dir |
Output directory where to out the file |
version |
Version of resulting image (in the case of multiple tests) |
A data cube
Rolf Simoes, rolf.simoes@inpe.br
## Not run:
# Retrieve the samples for Mato Grosso
# select band "ndvi"
samples_ndvi <- sits_select(samples_modis_4bands, bands = "NDVI")
# select a random forest model
rfor_model <- sits_train(samples_ndvi, sits_rfor(num_trees = 500))
# create a data cube based on the information about the files
data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
cube <- sits_cube(
source = "BDC",
collection = "MOD13Q1-6",
data_dir = data_dir,
delim = "_",
parse_info = c("X1", "X2", "tile", "band", "date")
)
# classify the raster image
probs_cube <- sits_classify(cube,
ml_model = rfor_model,
output_dir = tempdir(),
memsize = 4, multicores = 2
)
# label the classification
label_cube <- sits_label_classification(probs_cube, output_dir = tempdir())
## End(Not run)