sits_cluster_dendro

sits_cluster_dendro.sits

sits_cluster_dendro.default

These functions support hierarchical agglomerative clustering in
sits. They provide support from creating a dendrogram and using it for
cleaning samples.
<code>link[sits]{sits_cluster_dendro()}</code> takes a tibble with time series and
produces a sits tibble with an added "cluster" column. The function first
calculates a dendrogram and obtains a validity index for best clustering
using the adjusted Rand Index. After cutting the dendrogram using the chosen
validity index, it assigns a cluster to each sample.
<code>link[sits]{sits_cluster_frequency()}</code> computes the contingency
table between labels
and clusters and produces a matrix.
Its input is a tibble produced by <code>link[sits]{sits_cluster_dendro()}</code>.
<code>link[sits]{sits_cluster_clean()}</code> takes a tibble with time series
that has an additional `cluster` produced by
<code>link[sits]{sits_cluster_dendro()}</code>
and removes labels that are minority in each cluster.

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 Spatio-temporal Asset Catalog (STAC)
protocol (<https://stacspec.org/> and the 'gdalcubes' R package
developed by Appel and Pebesma (2019) <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 proposed by Pelletier et al (2019) <doi:10.3390/rs11050523>,
residual networks by Fawaz et al (2019) <doi:10.1007/s10618-019-00619-1>, and temporal attention encoders
by Garnot and Landrieu (2020) <doi:10.48550/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_dendro function

<dl><dt>samples</dt>
<dd>Tibble with input set of time series (class "sits").</dd>
<dt>bands</dt>
<dd>Bands to be used in the clustering
(character vector)</dd>
<dt>dist_method</dt>
<dd>One of the supported distances (single char vector)
"dtw": DTW with a Sakoe-Chiba constraint.
"dtw2": DTW with L2 norm and Sakoe-Chiba constraint.
"dtw_basic": A faster DTW with less functionality.
"lbk": Keogh's lower bound for DTW.
"lbi": Lemire's lower bound for DTW.</dd>
<dt>linkage</dt>
<dd>Agglomeration method to be used (single char vector)
One of "ward.D", "ward.D2", "single", "complete",
"average", "mcquitty", "median" or "centroid".</dd>
<dt>k</dt>
<dd>Desired number of clusters (overrides default value)</dd>
<dt>palette</dt>
<dd>Color palette as per `grDevices::hcl.pals()` function.</dd>
<dt>...</dt>
<dd>Additional parameters to be passed
to dtwclust::tsclust() function.</dd></dl>

Arguments

Rolf Simoes, <a href="/link/rolf.simoes%40inpe.br?package=sits&version=1.5.0" data-mini-rdoc="sits::rolf.simoes@inpe.br">rolf.simoes@inpe.br</a>

Author

Find clusters in time series samples — sits_cluster_dendro

<dl>

<dt>samples</dt>
<dd>Tibble with input set of time series (class "sits").</dd>


<dt>bands</dt>
<dd>Bands to be used in the clustering
(character vector)</dd>


<dt>dist_method</dt>
<dd>One of the supported distances (single char vector)
"dtw": DTW with a Sakoe-Chiba constraint.
"dtw2": DTW with L2 norm and Sakoe-Chiba constraint.
"dtw_basic": A faster DTW with less functionality.
"lbk": Keogh's lower bound for DTW.
"lbi": Lemire's lower bound for DTW.</dd>


<dt>linkage</dt>
<dd>Agglomeration method to be used (single char vector)
One of "ward.D", "ward.D2", "single", "complete",
"average", "mcquitty", "median" or "centroid".</dd>


<dt>k</dt>
<dd>Desired number of clusters (overrides default value)</dd>


<dt>palette</dt>
<dd>Color palette as per `grDevices::hcl.pals()` function.</dd>


<dt>...</dt>
<dd>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>

sits_cluster_dendro: Find clusters in time series samples

Description

Usage

Value

Arguments

Author

References

Examples