impute_mean

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.
Enables merging of multi-source imagery (SAR, optical, DEM).
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.
Includes object-based spatio-temporal segmentation for space-time OBIA.
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

Charlotte Pelletier

Estefania Pizarro

Karine Ferreira

Alber Sanchez

Alexandre Assuncao

Daniel Falbel

Gilberto Queiroz

Johannes Reiche

Pedro Andrade

Pedro Brito

Renato Assuncao

Ricardo Cartaxo

impute_mean function

<dl><dt>data</dt>
<dd>A time series vector or matrix</dd></dl>

Arguments

Felipe Carlos, <a href="/link/efelipecarlos%40gmail.com?package=sits&version=1.5.4" data-mini-rdoc="sits::efelipecarlos@gmail.com">efelipecarlos@gmail.com</a>

Author

Remove NA using mean — impute_mean

<dl>

<dt>data</dt>
<dd>A time series vector or matrix</dd>

</dl>

Felipe Carlos, <a href='mailto:efelipecarlos@gmail.com'>efelipecarlos@gmail.com</a>

Remove NA using mean

impute_mean: Remove NA using mean

Description

Usage

Value

Arguments

Author