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

Provided by: grass-doc_7.0.3-1build1_all

NAME

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

KEYWORDS

       raster, resample, kernel filter

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