sits_patterns: Create temporal patterns using a generalised additive model...

In e-sensing/sits.data: Satellite Image Time Series Data Handling

Description

This function takes a set of time series samples as input estimates a set of patterns. The patterns are calculated using a GAM model. The idea is to use a formula of type y ~ s(x), where x is a temporal reference and y if the value of the signal. For each time, there will be as many predictions as there are sample values. The GAM model predicts an approximation that fits the assumptions of the statistical model, based on a smooth function.

This method is based on the "createPatterns" method of the dtwSat package, which is described in the reference paper.

Usage

sits_patterns(data = NULL, freq = 8, formula = y ~ s(x), ...)

Arguments

data	A tibble in sits format with time series.
freq	Interval in days for the estimates to be generated.
formula	Formula to be applied in the estimate.
...	Any additional parameters.

Value

A sits tibble with the patterns.

Author(s)

Victor Maus, [e-mail]

Gilberto Camara, [e-mail]

Rolf Simoes, [e-mail]

References

Maus V, Camara G, Cartaxo R, Sanchez A, Ramos FM, de Queiroz GR (2016). A Time-Weighted Dynamic Time Warping Method for Land-Use and Land-Cover Mapping. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(8):3729-3739, August 2016. ISSN 1939-1404. doi:10.1109/JSTARS.2016.2517118.

Examples

# Read a set of samples for two classes
data(cerrado_2classes)
# Estimate a set of patterns (one for each label)
patterns <- sits_patterns(cerrado_2classes)
# Show the patterns
plot(patterns)

# Read a set of samples for Mato Grosso, Brazil, provided by EMBRAPA
data(samples_mt_4bands)
# Estimate a set of patterns (one for each label)
patterns <- sits_patterns(samples_mt_4bands)
# Show the patterns
plot(patterns)

e-sensing/sits.data documentation built on Dec. 26, 2019, 11:02 p.m.