r.resamp.filter(1grass) GRASS GIS User's Manual r.resamp.filter(1grass)
NAME
r.resamp.filter - Resamples raster map layers using an analytic ker-
nel.
KEYWORDS
raster, resample, kernel filter, filter, convolution, FIR, bartlett,
blackman, box, gauss, hamming, hann, hermite, lanczos, sinc
SYNOPSIS
r.resamp.filter
r.resamp.filter --help
r.resamp.filter [-n] input=name output=name filter=string[,string,...]
[radius=float[,float,...]] [x_radius=float[,float,...]] [y_ra-
dius=float[,float,...]] [--overwrite] [--help] [--verbose]
[--quiet] [--ui]
Flags:
-n
Propagate NULLs
--overwrite
Allow output files to overwrite existing files
--help
Print usage summary
--verbose
Verbose module output
--quiet
Quiet module output
--ui
Force launching GUI dialog
Parameters:
input=name [required]
Name of input raster map
output=name [required]
Name for output raster map
filter=string[,string,...] [required]
Filter kernel(s)
Options: box, bartlett, gauss, normal, hermite, sinc, lanczos1,
lanczos2, lanczos3, hann, hamming, blackman
radius=float[,float,...]
Filter radius
x_radius=float[,float,...]
Filter radius (horizontal)
y_radius=float[,float,...]
Filter radius (vertical)
DESCRIPTION
r.resamp.filter resamples an input raster, filtering the input with an
analytic kernel. Each output cell is typically calculated based upon a
small subset of the input cells, not the entire input. r.resamp.filter
performs convolution (i.e. a weighted sum is calculated for every
raster cell).
The module maps the input range to the width of the window function, so
wider windows will be "sharper" (have a higher cut-off frequency), e.g.
lanczos3 will be sharper than lanczos2.
r.resamp.filter implements FIR (finite impulse response) filtering. All
of the functions are low-pass filters, as they are symmetric. See
Wikipedia: Window function for examples of common window functions and
their frequency responses.
A piecewise-continuous function defined by sampled data can be consid-
ered a mixture (sum) of the underlying signal and quantisation noise.
The intent of a low pass filter is to discard the quantisation noise
while retaining the signal. The cut-off frequency is normally chosen
according to the sampling frequency, as the quantisation noise is domi-
nated by the sampling frequency and its harmonics. In general, the
cut-off frequency is inversely proportional to the width of the central
"lobe" of the window function.
When using r.resamp.filter with a specific radius, a specific cut-off
frequency regardless of the method is chosen. So while lanczos3 uses 3
times as large a window as lanczos1, the cut-off frequency remains the
same. Effectively, the radius is "normalised".
All of the kernels specified by the filter parameter are multiplied to-
gether. Typical usage will use either a single kernel or an infinite
kernel along with a finite window.
NOTES
Resampling modules (r.resample, r.resamp.stats, r.resamp.interp, r.re-
samp.rst, r.resamp.filter) resample the map to match the current region
settings.
When using a kernel which can have negative values (sinc, Lanczos), the
-n flag should be used. Otherwise, extreme values can arise due to the
total weight being close (or even equal) to zero.
Kernels with infinite extent (Gauss, normal, sinc, Hann, Hamming,
Blackman) must be used in conjunction with a finite windowing function
(box, Bartlett, Hermite, Lanczos).
The way that Lanczos filters are defined, the number of samples is sup-
posed to be proportional to the order ("a" parameter), so lanczos3
should use 3 times as many samples (at the same sampling frequency,
i.e. cover 3 times as large a time interval) as lanczos1 in order to
get a similar frequency response (higher-order filters will fall off
faster, but the frequency at which the fall-off starts should be the
same). See Wikipedia: Lanczos-kernel.svg for an illustration. If both
graphs were drawn on the same axes, they would have roughly the same
shape, but the a=3 window would have a longer tail. By scaling the axes
to the same width, the a=3 window has a narrower central lobe.
For longitude-latitude locations, the interpolation algorithm is based
on degree fractions, not on the absolute distances between cell cen-
ters. Any attempt to implement the latter would violate the integrity
of the interpolation method.
SEE ALSO
g.region, r.mfilter, r.resample, r.resamp.interp, r.resamp.rst, r.re-
samp.stats
Overview: Interpolation and Resampling in GRASS GIS
AUTHOR
Glynn Clements
SOURCE CODE
Available at: r.resamp.filter source code (history)
Accessed: unknown
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