.sits_twdtw_breaks

Returns a sits tibble with the results of the TWDTW classifier.
The TWDTW classifier uses the result of the sits_TWDTW_matches function to
find the best alignments of these matches in intervals chosen by the user

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_twdtw_breaks function

<dl><dt>matches</dt>
<dd>A dtwSat S4 object with the matches
produced by the sits_TWDTW_matches function.</dd>
<dt>samples</dt>
<dd>Input for the TWDTW matching function.</dd>
<dt>start_date</dt>
<dd>The start date of the classification period.</dd>
<dt>end_date</dt>
<dd>The end date of the classification period.</dd>
<dt>interval</dt>
<dd>The period between two classifications.</dd>
<dt>overlap</dt>
<dd>Minimum overlap between one match and
the interval of classification.</dd></dl>

Arguments

Victor Maus, <a href="/link/vwmaus1%40gmail.com?package=sits&version=1.1.0" data-mini-rdoc="sits::vwmaus1@gmail.com">vwmaus1@gmail.com</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

Classify a sits tibble using the matches found by the TWDTW methods — .sits_twdtw_breaks

<dl>

<dt>matches</dt>
<dd>A dtwSat S4 object with the matches
produced by the sits_TWDTW_matches function.</dd>


<dt>samples</dt>
<dd>Input for the TWDTW matching function.</dd>


<dt>start_date</dt>
<dd>The start date of the classification period.</dd>


<dt>end_date</dt>
<dd>The end date of the classification period.</dd>


<dt>interval</dt>
<dd>The period between two classifications.</dd>


<dt>overlap</dt>
<dd>Minimum overlap between one match and
the interval of classification.</dd>

</dl>

Victor Maus, <a href='mailto:vwmaus1@gmail.com'>vwmaus1@gmail.com</a>
Gilberto Camara, <a href='mailto:gilberto.camara@inpe.br'>gilberto.camara@inpe.br</a>

.sits_twdtw_breaks: Classify a sits tibble using the matches found by the TWDTW methods

Description

Usage

Value

Arguments

Author

References