.sits_cluster_dendrogram

Time series data and metadata
to be used to generate the dendrogram.

samples

bands

String with one of the supported distance
from proxy's dist, e.g. <code>TWDTW</code>.

dist_method

String with agglomeration method to be used.
Can be any `hclust` method (see `hclust`).
Default is 'ward.D2'.

linkage

Any additional parameters to be passed
to dtwclust::tsclust() function.

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><a rd-options="dtwclust" href="/link/tsclust?package=sits&version=0.13.0&to=dtwclust" data-mini-rdoc="dtwclust::tsclust">tsclust</a></code>).

internal

A set of tools for working with satellite image time series, both as individual data or as part of data cubes.
Builds data cubes from collections in AWS, Brazil Data Cube, and Digital Earth Africa.
Provides different visualisation methods for image time series.
Provides smoothing methods for noisy time series.
Includes functions for smoothing based on Bayesian inference.
Enables different clustering methods, including dendrograms and self-organized maps (SOM).
Matches noiseless patterns with noisy time series using the time-weighted dynamic time warping (TWDTW) method for shape recognition.
Provides machine learning methods for time series classification, including support vector machines (SVM), random forests and deep learning.
Minimum recommended requirements: 8 GB RAM and 1 CPU dual-core.

Gilberto Camara

sits

Satellite Image Time Series Analysis for Remote Sensing Data Cubes

Rolf Simoes

Felipe Souza

Lorena Santos

Alexandre Carvalho

Pedro Andrade

Karine Ferreira

Alber Sanchez

Gilberto Queiroz

.sits_cluster_dendrogram function

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><a rd-options='dtwclust' href='tsclust'>tsclust</a></code>).

.sits_cluster_dendrogram: Compute a dendrogram using hierarchical clustering

Description

Usage

Arguments

Value

References