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r.resamp.filter(1)	    GRASS GIS User's Manual	    r.resamp.filter(1)

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=nameA [required]
	   Name	of input raster	map

       output=nameA [required]
	   Name	for output raster map

       filter=string[,string,...]A [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)

       Main  index  | Raster index | Topics index | Keywords index | Graphical
       index | Full index

       A(C) 2003-2020 GRASS Development	Team, GRASS GIS	7.8.5 Reference	Manual

GRASS 7.8.5						    r.resamp.filter(1)

NAME | KEYWORDS | SYNOPSIS | DESCRIPTION | NOTES | SEE ALSO | AUTHOR | SOURCE CODE

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