| sits_som_clean_samples {sits} | R Documentation |
This function evaluate the quality of the samples based on the results of the SOM map. It produces a sits tibble with an evaluation column indicating if each sample is clean, should be analyzed or should be removed, and with a new column indicating the posterior probability of the sample
sits_som_clean_samples(
som_map,
prior_threshold = 0.6,
posterior_threshold = 0.6,
keep = c("clean", "analyze")
)
som_map |
An object returned
by |
prior_threshold |
Threshold of conditional probability (frequency of samples assigned to the same SOM neuron) |
posterior_threshold |
Threshold of posterior probability (influenced by the SOM neighborhood) |
keep |
Samples evaluation to be maintained in the data |
List with two sits tibbles. The first tibble has clean samples The second has samples that need to be analysed.
The algorithm identifies noisy samples, using 'prior_threshold' for the prior probability and 'posterior_threshold' for the posterior probability. Each sample receives an evaluation tag, according to the following rule: (a) If the prior probability is < 'prior_threshold', the sample is tagged as "remove"; (b) If the prior probability is >= 'prior_threshold' and the posterior probability is >='posterior_threshold', the sample is tagged as "clean"; (c) If the prior probability is >= 'posterior_threshold' and the posterior probability is < 'posterior_threshold', the sample is tagged as "analyze" for further inspection.
The user can define which tagged samples will be returned using the "keep" parameter, with the following options: "clean", "analyze", "remove".
Lorena Santos, lorena.santos@inpe.br
Karine Ferreira. karine.ferreira@inpe.br
## Not run:
# Read a set of samples
# Get a new subset of samples evaluated by clustering methods
som_map <- sits_som_map(samples_modis_4bands,
grid_xdim = 10, grid_ydim = 10,
distance = "euclidean"
)
new_samples <- sits_som_clean_samples(som_map)
## End(Not run)