Last chance! 50% off unlimited learning
Sale ends in
This function takes a set of time series samples as input estimates a set of patterns. The patterns are calculated based in 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 a suitable approximation that fits the assumptions of the statistical model. By default, the GAM method produces an approximation based on a smooth function.
This method is based on the "createPatterns" method of the dtwSat package, which is also described in the reference paper.
sits_patterns(data.tb = NULL, timeline = NULL, start_date = NULL,
end_date = NULL, freq = 8, formula = y ~ s(x), ...)A tibble in sits format with time series.
A timeline vector with the all dates for the coverage.
Starting date (month-day).
End date (month-day).
Interval in days for the estimates to be generated.
Formula to be applied in the estimate.
Any additional parameters.
A sits tibble with the patterns.
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.
# NOT RUN {
# Read a set of samples for two classes
data(cerrado_2classes)
# Estimate a set of patterns (one for each label)
patterns.tb <- sits_patterns(cerrado_2classes)
# Show the patterns
sits_plot(patterns.tb)
# Read a set of samples for the state of Mato Grosso, Brazil, provided by EMBRAPA
data(samples_mt_9classes)
# Estimate a set of patterns (one for each label)
patterns.tb <- sits_patterns(samples_mt_9classes)
# Show the patterns
sits_plot(patterns.tb)
# }
Run the code above in your browser using DataLab