sits_bayes_postprocess

raster_class

A matrix with the neighborhood window to compute bayesian smooth.
The central element index (i, j) is given by
i = floor(nrows(window)/2)+1 and j = floor(ncols(window)/2)+1.
Elements '0' are excluded from window.

window

noise

File to save the post processed raster.

file

Takes a set of classified raster layers, whose metadata is
 described by tibble (created by <code><a rd-options="sits" href="/link/sits_coverage?package=sits&version=1.12.0&to=sits" data-mini-rdoc="sits::sits_coverage">sits_coverage</a></code>),
 and a noise value, to do a bayesian smoothing process.

A set of tools for working with satellite image time series.
Supports data retrieval from a WTSS (web time series service) and other data sets.
Provides different visualisation methods for image time series.
Provides smoothing methods for noisy time series.
Enables different clustering methods, including dendrograms and SOM.
Matches noiseless patterns with noisy time series using the TWDTW method for shape recognition.
Provide machine learning methods for time series classification, including SVM, LDA, QDA, GLM, Lasso, Random Forests and Deep Learning.

Pedro Andrade

sits

Satellite Image Time Series Analysis

Rolf Simoes

Gilberto Camara

Victor Maus

Alexandre Carvalho

Lorena Santos

Gilberto Queiroz

Alber Sanchez

Luiz Assis

Adeline Marinho

sits_bayes_postprocess function

Takes a set of classified raster layers, whose metadata is
 described by tibble (created by <code><a rd-options='sits' href='sits_coverage'>sits_coverage</a></code>),
 and a noise value, to do a bayesian smoothing process.

Post-process a classified data raster with bayesian filter — sits_bayes_postprocess

Post-process a classified data raster with bayesian filter

sits_bayes_postprocess: Post-process a classified data raster with bayesian filter

Description

Usage

Arguments

Value