R: Clustering a set of satellite image time series using SOM

sits_som_cluster {sits} | R Documentation

## Clustering a set of satellite image time series using SOM

### Description

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.

### Usage

    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
    )

### Arguments

`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)

### Value

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

### Author(s)

Lorena Alves, lorena.santos@inpe.br

Karine Ferreira. karine.ferreira@inpe.br

### References

'kohonen' package (https://CRAN.R-project.org/package=kohonen)

### Examples

    ## Not run:
    # Evaluate the quality of the samples using SOM clustering
    new_samples <- sits_som_cluster(samples_modis_4bands)

    ## End(Not run)

[Package sits version 0.12.0 Index]