.sits_cube_probs

input tile (subset of a data cube).

tile

samples used for training the classification model.

samples

sub_image

output_dir

version

Take a tibble containing metadata about a data cube
containing time series 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.

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_cube_probs function

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

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

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

Description

Usage

Arguments

Value