.sits_timeline_match

timeline of input observations (vector).

timeline

reference for starting the classification.

ref_start_date

reference for end the classification.

ref_end_date

num_samples

For correct classification, the input data set
 should be aligned to that of the reference data set.
 This function aligns these data sets.

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_match function

Find dates in the input data cube that match those of the patterns — .sits_timeline_match

Find dates in the input data cube that match those of the patterns

.sits_timeline_match: Find dates in the input data cube that match those of the patterns

Description

Usage

Arguments

Value