.sits_plot_som_map

SOM map produced by "sits_som_map" function

Type of plot ("codes" or "mapping")

type

whatmap

Given a kohonen object with a set of time neurons, plot them.
The plot function produces different plots based on the input data:<ul>
<li>"codes": Plot the vector weight for each neuron.</li>
<li>"mapping": Shows where samples are mapped.</li>
</ul>

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_plot_som_map function

Plot the SOM grid with neurons labeled — .sits_plot_som_map

Plot the SOM grid with neurons labeled

.sits_plot_som_map: Plot the SOM grid with neurons labeled

Description

Usage

Arguments