.sits_cluster_dendrogram

Cluster time series in hierarchical mode.
Hierarchical clustering, as its name suggests,
is an algorithm that tries to create a hierarchy of groups in which,
as the level in the hierarchy increases, clusters are created by merging
the clusters from the next lower level, producing
an ordered sequence of groupings. The similarity measure used to
group time series in a cluster is the dtw metric.
The procedure is deterministic, so it will always give the same
result for a chosen set of similarity measures
(see <code>tsclust</code>).

internal

An end-to-end toolkit for land use and land cover classification
using big Earth observation data, based on machine learning methods
applied to satellite image data cubes, as described in Simoes et al (2021) <doi:10.3390/rs13132428>.
Builds regular data cubes from collections in AWS, Microsoft Planetary Computer,
Brazil Data Cube, and Digital Earth Africa using the STAC protocol <https://stacspec.org/>
and the 'gdalcubes' R package <doi:10.3390/data4030092>.
Supports visualization methods for images and time series and
smoothing filters for dealing with noisy time series.
Includes functions for quality assessment of training samples using self-organized maps
as presented by Santos et al (2021) <doi:10.1016/j.isprsjprs.2021.04.014>.
Provides machine learning methods including support vector machines,
random forests, extreme gradient boosting, multi-layer perceptrons,
temporal convolutional neural networks <doi:10.3390/rs11050523>,
residual networks <arxiv:1809.04356>, and temporal attention encoders
<arXiv:2007.00586>.
Performs efficient classification of big Earth observation data cubes and includes
functions for post-classification smoothing based on Bayesian inference, and
methods for uncertainty assessment. Enables best
practices for estimating area and assessing accuracy of land change as
recommended by Olofsson et al(2014) <doi:10.1016/j.rse.2014.02.015>.
Minimum recommended requirements: 16 GB RAM and 4 CPU dual-core.

Gilberto Camara

sits

Satellite Image Time Series Analysis for Earth Observation Data
Cubes

Rolf Simoes

Felipe Souza

Lorena Santos

Pedro Andrade

Karine Ferreira

Alber Sanchez

Gilberto Queiroz

.sits_cluster_dendrogram function

<dl><dt>samples</dt>
<dd>Time series data and metadata
to be used to generate the dendrogram.</dd>
<dt>bands</dt>
<dd>Vector of bands to be clustered.</dd>
<dt>dist_method</dt>
<dd>One of the supported distance
from proxy's dist, e.g. <code>TWDTW</code>.</dd>
<dt>linkage</dt>
<dd>Agglomeration method to be used.
Can be any `hclust` method (see `hclust`).
Default is 'ward.D2'.</dd>
<dt>...</dt>
<dd>Any additional parameters to be passed
to dtwclust::tsclust() function.</dd></dl>

Arguments

Rolf Simoes, <a href="/link/rolf.simoes%40inpe.br?package=sits&version=1.1.0" data-mini-rdoc="sits::rolf.simoes@inpe.br">rolf.simoes@inpe.br</a>
Gilberto Camara, <a href="/link/gilberto.camara%40inpe.br?package=sits&version=1.1.0" data-mini-rdoc="sits::gilberto.camara@inpe.br">gilberto.camara@inpe.br</a>

Author

Compute a dendrogram using hierarchical clustering — .sits_cluster_dendrogram

<dl>

<dt>samples</dt>
<dd>Time series data and metadata
to be used to generate the dendrogram.</dd>


<dt>bands</dt>
<dd>Vector of bands to be clustered.</dd>


<dt>dist_method</dt>
<dd>One of the supported distance
from proxy's dist, e.g. <code>TWDTW</code>.</dd>


<dt>linkage</dt>
<dd>Agglomeration method to be used.
Can be any `hclust` method (see `hclust`).
Default is 'ward.D2'.</dd>


<dt>...</dt>
<dd>Any additional parameters to be passed
to dtwclust::tsclust() function.</dd>

</dl>

Rolf Simoes, <a href='mailto:rolf.simoes@inpe.br'>rolf.simoes@inpe.br</a>
Gilberto Camara, <a href='mailto:gilberto.camara@inpe.br'>gilberto.camara@inpe.br</a>

.sits_cluster_dendrogram: Compute a dendrogram using hierarchical clustering

Description

Usage

Value

Arguments

Author

References