| sits_som_cluster {sits} | R Documentation |
This function uses self-organized maps to find clusters in
satellite image time series for quality control of the samples.
Calls sits_som_map to generate the som map and
sits_som_clean_samples to produce a clean set of samples.
The parameters "grid_xdim", "grid_ydim", "rlen", "distance", "alpha", and
"iterations" are used by sits_som_map to control
how the Kohonen map is generated.
The parameters "prior_threshold" and "posterior_threshold" control
how the good quality samples are selected, based on the Kohonen map.
sits_som_cluster( data, grid_xdim = 10, grid_ydim = 10, alpha = 1, distance = "euclidean", rlen = 100, prior_threshold = 0.6, posterior_threshold = 0.6, som_radius = 2 )
data |
A tibble with samples to be clustered. |
grid_xdim |
X dimension of the SOM grid (default = 25). |
grid_ydim |
Y dimension of the SOM grid. |
alpha |
Starting learning rate, which decreases according to number of iterations. |
distance |
The similarity measure (distance). |
rlen |
How many times dataset will be presented to the SOM. |
prior_threshold |
Threshold of priot probability (frequency of samples assigned to a same SOM neuron) |
posterior_threshold |
Threshold of posterior probability (influenced by the SOM neighborhood) |
som_radius |
Radius of neighborhood on the SOM map (controls the size of the neighbourhood) |
A sits tibble with an evaluation column indicating if each samples is clean, should be analyzed or should be removed, and with a new column indicating the posterior probability of the sample
Lorena Alves, lorena.santos@inpe.br
Karine Ferreira. karine.ferreira@inpe.br
'kohonen' package (https://CRAN.R-project.org/package=kohonen)
## Not run: # Evaluate the quality of the samples using SOM clustering new_samples <- sits_som_cluster(samples_modis_4bands) ## End(Not run)