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       r.resamp.filter  - Resamples raster map layers using an analytic kernel.


       raster, resample, kernel filter, filter, convolution, FIR, bartlett, blackman, box, gauss,
       hamming, hann, hermite, lanczos, sinc


       r.resamp.filter --help
       r.resamp.filter      [-n]      input=name      output=name      filter=string[,string,...]
       [radius=float[,float,...]]    [x_radius=float[,float,...]]    [y_radius=float[,float,...]]
       [--overwrite]  [--help]  [--verbose]  [--quiet]  [--ui]

           Propagate NULLs

           Allow output files to overwrite existing files

           Print usage summary

           Verbose module output

           Quiet module output

           Force launching GUI dialog

       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

           Filter radius

           Filter radius (horizontal)

           Filter radius (vertical)


       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

       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 considered 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
       dominated 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 together. Typical
       usage will use either a single kernel or an infinite kernel along with a finite window.


       Resampling   modules   (r.resample,   r.resamp.stats,    r.resamp.interp,    r.resamp.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  supposed  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 centers.  Any attempt  to  implement
       the latter would violate the integrity of the interpolation method.


        g.region, r.mfilter, r.resample, r.resamp.interp, r.resamp.rst, r.resamp.stats

       Overview: Interpolation and Resampling in GRASS GIS


       Glynn Clements


       Available at: r.resamp.filter source code (history)

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