sits_som_clean_samples

Cleans the samples based on SOM map information

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) <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_som_clean_samples function

<dl><dt>som_map</dt>
<dd>Returned by <code>sits_som_map</code>.</dd>
<dt>prior_threshold</dt>
<dd>Threshold of conditional probability
(frequency of samples assigned to the same SOM neuron).</dd>
<dt>posterior_threshold</dt>
<dd>Threshold of posterior probability
(influenced by the SOM neighborhood).</dd>
<dt>keep</dt>
<dd>Which types of evaluation to be maintained in the data.</dd></dl>

Arguments

Cleans the samples based on SOM map information — sits_som_clean_samples

<dl>

<dt>som_map</dt>
<dd>Returned by <code>sits_som_map</code>.</dd>


<dt>prior_threshold</dt>
<dd>Threshold of conditional probability
(frequency of samples assigned to the same SOM neuron).</dd>


<dt>posterior_threshold</dt>
<dd>Threshold of posterior probability
(influenced by the SOM neighborhood).</dd>


<dt>keep</dt>
<dd>Which types of evaluation to be maintained in the data.</dd>

</dl>

sits_som_clean_samples: Cleans the samples based on SOM map information

Description

Usage

Value

Arguments