These function use self-organized maps to perform quality analysis in satellite image time series.
Usage
sits_som_map(
data,
grid_xdim = 10L,
grid_ydim = 10L,
alpha = 1,
rlen = 100L,
distance = "dtw",
som_radius = 2L,
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
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 sample quality
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".
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.
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. doi:10.1016/j.isprsjprs.2021.04.014 .
Author
Lorena Alves, lorena.santos@inpe.br
Karine Ferreira. karine.ferreira@inpe.br
Gilberto Camara, gilberto.camara@inpe.br
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)
}