.sits_raster_blocks

tile

ml_model

sub_image

memory available for classification (in GB).

memsize

number of threads to process the time series.

multicores

Defines the size of the block of an image to be read.
For example, a Raster Brick with 500 rows and 500 columns
and 400 time instances will have a total pixel size
of 800 Mb if pixels are 64-bit.

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_raster_blocks function

Define a reasonable block size to process an image subset — .sits_raster_blocks

Define a reasonable block size to process an image subset

.sits_raster_blocks: Define a reasonable block size to process an image subset

Description

Usage

Arguments

Value