| sits_patterns {sits} | R Documentation |
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 a suitable 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 also described in the reference paper.
sits_patterns(data = NULL, freq = 8, formula = y ~ s(x), ...)
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. |
A sits tibble with the patterns.
Victor Maus, vwmaus1@gmail.com
Gilberto Camara, gilberto.camara@inpe.br
Rolf Simoes, rolf.simoes@inpe.br
Maus V, Camara G, Cartaxo R, Sanchez A, Ramos F, Queiroz GR. 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 <- sits_patterns(cerrado_2classes) # Show the patterns plot(patterns) # Read a set of samples for the state of Mato Grosso, Brazil data(samples_modis_4bands) # Estimate a set of patterns (one for each label) patterns <- sits_patterns(samples_modis_4bands) # Show the patterns plot(patterns) ## End(Not run)