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

Provided by: grass-doc_8.3.2-1ubuntu2_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, parallel

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,...]]   [memory=memory  in  MB]    [nprocs=integer]
       [--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)

       memory=memory in MB
           Maximum memory to be used (in MB)
           Cache size for raster rows
           Default: 300

       nprocs=integer
           Number of threads for parallel computing
           Default: 1

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.

PERFORMANCE
By specifying the number of parallel processes with nprocs option, r.resamp.filter can run faster, see
benchmarks below.
Figure: Benchmark shows execution time for different number of cells. See benchmark script in the source
code.

To reduce the memory requirements to minimum, set option memory to zero. To take advantage of the
parallelization, GRASS GIS needs to compiled with OpenMP enabled.

AUTHOR

       Glynn Clements

SOURCE CODE

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

       Accessed: Monday Apr 01 03:07:53 2024

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       © 2003-2024 GRASS Development Team, GRASS GIS 8.3.2 Reference Manual

GRASS 8.3.2                                                                              r.resamp.filter(1grass)