sits (version 1.1.0)

sits_filters: Filter time series and data cubes

Description

Filtering functions should be used with `sits_filter()`. The following filtering functions is supported by `sits`:

`sits_whittaker()`: 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.

`sits_filter()`: applies a filter to all bands.

`sits_sgolay()`: 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_whittaker(data = NULL, lambda = 0.5)
sits_filter(data, filter = sits_whittaker())
sits_sgolay(data = NULL, order = 3, length = 5)

Value

Filtered time series

Arguments

data

Time series or matrix.

lambda

Smoothing factor to be applied (default 0.5).

filter

a filter function such as `sits_whittaker()` or `sits_sgolay()`.

order

Filter order (integer).

length

Filter length (must be odd).

Author

Rolf Simoes,

Gilberto Camara,

Felipe Carvalho,

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.

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

See Also

sits_apply

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)

# 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)