.sits_label_cube

Metadata about the input data cube (probability).

probs_cube

Output directory where to put the files

output_dir

version

Takes a tibble containing metadata about a data cube wuth
classification probabilites and and creates a
data cube to store the classification result.

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_label_cube function

Create a set of RasterLayer objects
       to store data cube classification results (labelled classes) — .sits_label_cube

Create a set of RasterLayer objects
       to store data cube classification results (labelled classes)

.sits_label_cube: Create a set of RasterLayer objects to store data cube classification results (labelled classes)

Description

Usage

Arguments

Value