sits_sgolay: Filter time series with Savitzky-Golay filter

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

Filter time series with Savitzky-Golay filter

Description

An optimal polynomial for warping a time series. The degree of smoothing depends on the filter order (usually 3.0). The order of the polynomial uses the parameter 'order' (default = 3), the size of the temporal window uses the parameter 'length' (default = 5).

Usage

sits_sgolay(data = NULL, order = 3, length = 5)

Arguments

data	Time series or matrix.
order	Filter order (integer).
length	Filter length (must be odd).

Value

Filtered time series

Author(s)

Rolf Simoes, [e-mail]

Gilberto Camara, [e-mail]

Felipe Carvalho, [e-mail]

References

A. Savitzky, M. Golay, "Smoothing and Differentiation of Data by Simplified Least Squares Procedures". Analytical Chemistry, 36 (8): 1627–39, 1964.

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 Savitzky-Golay smoother
    point_sg <- sits_filter(point_ndvi,
        filter = sits_sgolay(order = 3, length = 5)
    )
    # Merge time series
    point_ndvi <- sits_merge(point_ndvi, point_sg, suffix = c("", ".SG"))

    # 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.