sits_impute

An end-to-end toolkit for land use and land cover classification
using big Earth observation data. Builds satellite image data cubes from cloud collections.
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 and
to reduce training samples imbalance. Provides machine learning algorithms including support vector machines,
random forests, extreme gradient boosting, multi-layer perceptrons,
temporal convolution neural networks, and temporal attention encoders.
Performs efficient classification of big Earth observation data cubes and includes
functions for post-classification smoothing based on Bayesian inference.
Enables best practices for estimating area and assessing accuracy of land change.
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

Felipe Carlos

Lorena Santos

Karine Ferreira

Charlotte Pelletier

Pedro Andrade

Alber Sanchez

Estefania Pizarro

Gilberto Queiroz

sits_impute function

<dl><dt>samples</dt>
<dd>A time series tibble</dd>
<dt>impute_fn</dt>
<dd>Imputation function</dd></dl>

Arguments

Gilberto Camara, <a href="/link/gilberto.camara%40inpe.br?package=sits&version=1.5.2" data-mini-rdoc="sits::gilberto.camara@inpe.br">gilberto.camara@inpe.br</a>

Author

Replace NA values in time series with imputation function — sits_impute

<dl>

<dt>samples</dt>
<dd>A time series tibble</dd>


<dt>impute_fn</dt>
<dd>Imputation function</dd>

</dl>

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

sits_impute: Replace NA values in time series with imputation function

Description

Usage

Value

Arguments

Author