sits_whittaker: Filter time series with whittaker filter

In e-sensing/sits: Satellite Image Time Series Analysis for Earth Observation Data Cubes

Filter time series with whittaker filter

Description

The algorithm searches for an optimal warping polynomial. The degree of smoothing depends on smoothing factor lambda (usually from 0.5 to 10.0). Use lambda = 0.5 for very slight smoothing and lambda = 5.0 for strong smoothing.

Usage

sits_whittaker(data = NULL, lambda = 0.5)

Arguments

data	Time series or matrix.
lambda	Smoothing factor to be applied (default 0.5).

Value

Filtered time series

Author(s)

Rolf Simoes, [e-mail]

Gilberto Camara, [e-mail]

Felipe Carvalho, [e-mail]

References

Francesco Vuolo, Wai-Tim Ng, Clement Atzberger, "Smoothing and gap-filling of high resolution multi-spectral time series: Example of Landsat data", Int Journal of Applied Earth Observation and Geoinformation, vol. 57, pg. 202-213, 2107.

See Also

sits_apply

Examples

if (sits_run_examples()) {
    # Retrieve a time series with values of NDVI
    point_ndvi <- sits_select(point_mt_6bands, bands = "NDVI")
    # Filter the point using the Whittaker smoother
    point_whit <- sits_filter(point_ndvi, sits_whittaker(lambda = 3.0))
    # Merge time series
    point_ndvi <- sits_merge(point_ndvi, point_whit,
                            suffix = c("", ".WHIT"))
    # Plot the two points to see the smoothing effect
    plot(point_ndvi)
}

e-sensing/sits documentation built on Jan. 28, 2024, 6:05 a.m.