.sits_gc_cube

A <code>character</code> with the The period of time in which it
is desired to apply in the cube, must be provided based on ISO8601, where 1
number and a unit are provided, for example "P16D".

period

A <code>character</code> with the method that will be applied in
the aggregation, the following are available: "min", "max", "mean",
"median" or "first".

method

A <code>character</code> with the method that will be applied
in the resampling in mosaic operation. The following are available: "near",
"bilinear", "bicubic" or others supported by gdalwarp
(see https://gdal.org/programs/gdalwarp.html).

resampling

A <code>character</code> with the path and name where the
database will be create. E.g. "my/path/gdalcubes.db"

path_db

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_gc_cube function

Create a cube_view object — .sits_gc_cube

Create a cube_view object

.sits_gc_cube: Create a cube_view object

Description

Usage

Arguments

Value