| sits_som_map {sits} | R Documentation |
This function uses package self-organized maps to find clusters in satellite image time series to cluster the samples. It also evaluates the quality of each sample using SOM properties.
The results is a list with three members: (1) the samples tibble, with one additional column indicating to which neuron it has been mapped; (2) the Kohonen map, used for plotting and cluster quality measures; (3) a tibble with the labelled neurons, where each class of each neuron is associated to two values: (a) the prior probability that this class belongs to a cluster based on the frequency of samples of this class allocated to the neuron; (b) the posterior probability that this class belongs to a cluster, using data for the neighbours on the SOM map.
sits_som_map( data, grid_xdim = 10, grid_ydim = 10, alpha = 1, rlen = 100, distance = "euclidean", som_radius = 2, mode = "online" )
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 (decreases according to number of iterations). |
rlen |
Number of iterations to produce the SOM. |
distance |
The type of similarity measure (distance). |
som_radius |
Radius of SOM neighborhood |
mode |
Type of learning algorithm (default = "online") |
A list of tibbles containing statistics about the samples and the neuron in each iteration.
Lorena Alves, lorena.santos@inpe.br
Karine Ferreira. karine.ferreira@inpe.br
'kohonen' package (https://CRAN.R-project.org/package=kohonen)
## Not run: # Produce a cluster map som_cluster <- sits_som_map(samples_modis_4bands) # plot the som map plot(som_cluster) # Clean the samples to get better quality ones clean_samples <- sits_som_clean_samples(som_cluster) ## End(Not run)