# This is a demonstration of classification of using
# images from the Brazil Data Cube with the STAC catalogue
# The input is a CBERS-4 data set covering an area in the Cerrado
# of the state of Bahia (Brazil)
# with two bands (NDVI and EVI)
library(sits)
# load the sitsdata library
if (!requireNamespace("sitsdata", quietly = TRUE)) {
stop("Please install package sitsdata\n",
"Please call devtools::install_github('e-sensing/sitsdata')",
call. = FALSE
)
}
# load the sitsdata library
library(sitsdata)
# load the samples
data("samples_cerrado_cbers")
# set up the bands
bands <- c("NDVI", "EVI")
# select only the samples for the chosen bands
cbers_samples_2bands <- sits_select(
data = samples_cerrado_cbers,
bands = bands
)
# define the start and end dates for selection the images
timeline_samples <- sits_timeline(cbers_samples_2bands)
start_date <- timeline_samples[[1L]]
end_date <- timeline_samples[length(timeline_samples)]
# define a CBERS data cube using the Brazil Data Cube
cbers_cube <- sits_cube(
source = "BDC",
collection = "CBERS-WFI-16D",
bands = bands,
tiles = "007004",
start_date = start_date,
end_date = end_date
)
# train an RFOR model
rfor_model <- sits_train(
samples = cbers_samples_2bands,
ml_method = sits_rfor()
)
# classify the data (remember to set the appropriate memory size)
cbers_probs <- sits_classify(
data = cbers_cube,
ml_model = rfor_model,
memsize = 16,
multicores = 4,
output_dir = tempdir(),
verbose = TRUE,
progress = TRUE
)
# plot the classification result
plot(cbers_probs)
# post process probabilities map with bayesian smoothing
cbers_bayes <- sits_smooth(
cube = cbers_probs,
memsize = 16,
multicores = 4,
output_dir = tempdir()
)
# plot the classification result after smoothing
plot(cbers_bayes)
# label the smoothed image
cbers_lbayes <- sits_label_classification(
cube = cbers_bayes,
memsize = 16,
multicores = 4,
output_dir = tempdir()
)
# plot the labelled image
plot(cbers_lbayes)
timeline <- sits_timeline(cbers_cube)
# view the classification results together with the original maps
sits_view(
x = cbers_cube,
red = "EVI",
green = "NDVI",
blue = "EVI",
dates = c(timeline[[1]], timeline[[length(timeline)]]),
class_cube = cbers_lbayes
)
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