sits_som: Use SOM for quality analysis of time series samples

In sits: Satellite Image Time Series Analysis for Earth Observation Data Cubes

Use SOM for quality analysis of time series samples

Description

These function use self-organized maps to perform quality analysis in satellite image time series

sits_som_map() creates a SOM map, where high-dimensional data is mapped into a two dimensional map, keeping the topological relations between data patterns. Each sample is assigned to a neuron, and neurons are placed in the grid based on similarity.

sits_som_evaluate_cluster() analyses the neurons of the SOM map, and builds clusters based on them. Each cluster is a neuron or a set of neuron categorized with same label. It produces a tibble with the percentage of mixture of classes in each cluster.

sits_som_clean_samples() evaluates the quality of the samples based on the results of the SOM map. 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".

Usage

sits_som_map(
  data,
  grid_xdim = 10,
  grid_ydim = 10,
  alpha = 1,
  rlen = 100,
  distance = "dtw",
  som_radius = 2,
  mode = "online"
)

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 (decreases according to number of iterations).
rlen	Number of iterations to produce the SOM.
distance	The type of similarity measure (distance). The following similarity measurements are supported: "euclidean" and "dtw". The default similarity measure is "dtw".
som_radius	Radius of SOM neighborhood.
mode	Type of learning algorithm. The following learning algorithm are available: "online", "batch", and "pbatch". The default learning algorithm is "online".

Value

sits_som_map() produces a list with three members: (1) the samples tibble, with one additional column indicating to which neuron each sample 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.

Note

To learn more about the learning algorithms, check the kohonen::supersom function.

The sits package implements the "dtw" (Dynamic Time Warping) similarity measure. The "euclidean" similarity measurement come from the kohonen::supersom (dist.fcts) function.

Author(s)

Lorena Alves, [e-mail]

Karine Ferreira. [e-mail]

References

Lorena Santos, Karine Ferreira, Gilberto Camara, Michelle Picoli, Rolf Simoes, “Quality control and class noise reduction of satellite image time series”. ISPRS Journal of Photogrammetry and Remote Sensing, vol. 177, pp 75-88, 2021. https://doi.org/10.1016/j.isprsjprs.2021.04.014.

Examples

if (sits_run_examples()) {
    # create a som map
    som_map <- sits_som_map(samples_modis_ndvi)
    # plot the som map
    plot(som_map)
    # evaluate the som map and create clusters
    clusters_som <- sits_som_evaluate_cluster(som_map)
    # plot the cluster evaluation
    plot(clusters_som)
    # clean the samples
    new_samples <- sits_som_clean_samples(som_map)
}

sits documentation built on Sept. 11, 2024, 6:36 p.m.