.sits_clouds_blocks_estimate

cube

<p>number of bands in memory for processing</p>

n_bands

n_instances

memsize

<p>Estimate the number of blocks to correct for clouds</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_clouds_blocks_estimate function

Estimate the number of blocks to correct for clouds — .sits_clouds_blocks_estimate

Estimate the number of blocks to correct for clouds

.sits_clouds_blocks_estimate: Estimate the number of blocks to correct for clouds

Description

Usage

Arguments

Value