.sits_som_bayes_estimate

data

Object that contains all parameters of SOM
provided by "kohonen" package

kohonen_obj

A tibble containing information about each neuron.

labelled_neurons

Distance in the SOM map to consider neighbours

som_radius

Computes the probability of a sample belongs
 to a cluster using bayesian filter.

internal

A set of tools for working with satellite image time series, both as individual data or as part of data cubes.
Builds data cubes from collections in AWS, Brazil Data Cube, and Digital Earth Africa.
Provides different visualisation methods for image time series.
Provides smoothing methods for noisy time series.
Includes functions for smoothing based on Bayesian inference.
Enables different clustering methods, including dendrograms and self-organized maps (SOM).
Matches noiseless patterns with noisy time series using the time-weighted dynamic time warping (TWDTW) method for shape recognition.
Provides machine learning methods for time series classification, including support vector machines (SVM), random forests and deep learning.
Minimum recommended requirements: 8 GB RAM and 1 CPU dual-core.

Gilberto Camara

sits

Satellite Image Time Series Analysis for Remote Sensing Data Cubes

Rolf Simoes

Felipe Souza

Lorena Santos

Alexandre Carvalho

Pedro Andrade

Karine Ferreira

Alber Sanchez

Gilberto Queiroz

.sits_som_bayes_estimate function

Probability of a sample belongs to a cluster using bayesian filter — .sits_som_bayes_estimate

Probability of a sample belongs to a cluster using bayesian filter

.sits_som_bayes_estimate: Probability of a sample belongs to a cluster using bayesian filter

Description

Usage

Arguments

Value