1r.resamp.filter(1) GRASS GIS User's Manual r.resamp.filter(1)
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6 r.resamp.filter - Resamples raster map layers using an analytic ker‐
7 nel.
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10 raster, resample, kernel filter, filter, convolution, FIR, bartlett,
11 blackman, box, gauss, hamming, hann, hermite, lanczos, sinc
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14 r.resamp.filter
15 r.resamp.filter --help
16 r.resamp.filter [-n] input=name output=name filter=string[,string,...]
17 [radius=float[,float,...]] [x_radius=float[,float,...]] [y_ra‐
18 dius=float[,float,...]] [--overwrite] [--help] [--verbose]
19 [--quiet] [--ui]
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21 Flags:
22 -n
23 Propagate NULLs
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25 --overwrite
26 Allow output files to overwrite existing files
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28 --help
29 Print usage summary
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31 --verbose
32 Verbose module output
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34 --quiet
35 Quiet module output
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37 --ui
38 Force launching GUI dialog
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40 Parameters:
41 input=name [required]
42 Name of input raster map
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44 output=name [required]
45 Name for output raster map
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47 filter=string[,string,...]Â [required]
48 Filter kernel(s)
49 Options: box, bartlett, gauss, normal, hermite, sinc, lanczos1,
50 lanczos2, lanczos3, hann, hamming, blackman
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52 radius=float[,float,...]
53 Filter radius
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55 x_radius=float[,float,...]
56 Filter radius (horizontal)
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58 y_radius=float[,float,...]
59 Filter radius (vertical)
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62 r.resamp.filter resamples an input raster, filtering the input with an
63 analytic kernel. Each output cell is typically calculated based upon a
64 small subset of the input cells, not the entire input. r.resamp.filter
65 performs convolution (i.e. a weighted sum is calculated for every
66 raster cell).
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68 The module maps the input range to the width of the window function, so
69 wider windows will be "sharper" (have a higher cut-off frequency), e.g.
70 lanczos3 will be sharper than lanczos2.
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72 r.resamp.filter implements FIR (finite impulse response) filtering. All
73 of the functions are low-pass filters, as they are symmetric. See
74 Wikipedia: Window function for examples of common window functions and
75 their frequency responses.
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77 A piecewise-continuous function defined by sampled data can be consid‐
78 ered a mixture (sum) of the underlying signal and quantisation noise.
79 The intent of a low pass filter is to discard the quantisation noise
80 while retaining the signal. The cut-off frequency is normally chosen
81 according to the sampling frequency, as the quantisation noise is domi‐
82 nated by the sampling frequency and its harmonics. In general, the
83 cut-off frequency is inversely proportional to the width of the central
84 "lobe" of the window function.
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86 When using r.resamp.filter with a specific radius, a specific cut-off
87 frequency regardless of the method is chosen. So while lanczos3 uses 3
88 times as large a window as lanczos1, the cut-off frequency remains the
89 same. Effectively, the radius is "normalised".
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91 All of the kernels specified by the filter parameter are multiplied to‐
92 gether. Typical usage will use either a single kernel or an infinite
93 kernel along with a finite window.
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96 Resampling modules (r.resample, r.resamp.stats, r.resamp.interp, r.re‐
97 samp.rst, r.resamp.filter) resample the map to match the current region
98 settings.
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100 When using a kernel which can have negative values (sinc, Lanczos), the
101 -n flag should be used. Otherwise, extreme values can arise due to the
102 total weight being close (or even equal) to zero.
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104 Kernels with infinite extent (Gauss, normal, sinc, Hann, Hamming,
105 Blackman) must be used in conjunction with a finite windowing function
106 (box, Bartlett, Hermite, Lanczos).
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108 The way that Lanczos filters are defined, the number of samples is sup‐
109 posed to be proportional to the order ("a" parameter), so lanczos3
110 should use 3 times as many samples (at the same sampling frequency,
111 i.e. cover 3 times as large a time interval) as lanczos1 in order to
112 get a similar frequency response (higher-order filters will fall off
113 faster, but the frequency at which the fall-off starts should be the
114 same). See Wikipedia: Lanczos-kernel.svg for an illustration. If both
115 graphs were drawn on the same axes, they would have roughly the same
116 shape, but the a=3 window would have a longer tail. By scaling the axes
117 to the same width, the a=3 window has a narrower central lobe.
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119 For longitude-latitude locations, the interpolation algorithm is based
120 on degree fractions, not on the absolute distances between cell cen‐
121 ters. Any attempt to implement the latter would violate the integrity
122 of the interpolation method.
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125 g.region, r.mfilter, r.resample, r.resamp.interp, r.resamp.rst, r.re‐
126 samp.stats
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128 Overview: Interpolation and Resampling in GRASS GIS
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131 Glynn Clements
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134 Available at: r.resamp.filter source code (history)
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136 Accessed: Mon Jun 20 16:46:23 2022
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138 Main index | Raster index | Topics index | Keywords index | Graphical
139 index | Full index
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141 © 2003-2022 GRASS Development Team, GRASS GIS 8.2.0 Reference Manual
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145GRASS 8.2.0 r.resamp.filter(1)