.sits_timeline_dist_indexes

<p>Tibble with classification information.</p>

class_info

ntimes

<p>Given a list of time indexes that indicate
             the start and end of the values to
             be extracted to classify each band,
             obtain a list of indexes that will be used to
             extract values from a combined distance tibble
             (which has all the bands put together).</p>

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_timeline_dist_indexes function

Indexes to extract data from a distance table for classification — .sits_timeline_dist_indexes

Indexes to extract data from a distance table for classification

.sits_timeline_dist_indexes: Indexes to extract data from a distance table for classification

Description

Usage

Arguments

Value