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

Provided by: grass-doc_7.8.2-1build3_all

NAME

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

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_radius=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  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.

NOTES

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.

AUTHOR

       Glynn Clements

SOURCE CODE

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

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       © 2003-2019 GRASS Development Team, GRASS GIS 7.8.2 Reference Manual