.sits_cube_classified

cube

<p>samples used for training the classification model.</p>

samples

name

sub_image

output_dir

version

<p>Take a tibble containing metadata about a data cube
containing time series (each Brick has information for one band) and create a
set of RasterLayers to store the classification result.
Each RasterLayer corresponds to one time step.
The time steps are specified in a list of dates.</p>

internal

A set of tools for working with satellite image time series, both as individual data or as part of data cubes.
Supports data retrieval from a web time series service (WTSS), raster bricks and cubes from the Brazilan Data Cube.
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

Pedro Andrade

Lorena Santos

Alexandre Carvalho

Gilberto Queiroz

Karine Ferreira

Alber Sanchez

Victor Maus

.sits_cube_classified function

Create a set of RasterLayer objects to store
data cube classification results (only the probs) — .sits_cube_classified

Create a set of RasterLayer objects to store
data cube classification results (only the probs)

.sits_cube_classified: Create a set of RasterLayer objects to store data cube classification results (only the probs)

Description

Usage

Arguments

Value