pktools  2.6.7
Processing Kernel for geospatial data

program to filter raster images


Usage: pkfilter -i input -o output [-f filter | -perc value | -srf file [-srf file]* -win wavelength [-win wavelength]* | -wout wavelength -fwhm value [-wout wavelength -fwhm value]* -win wavelength [-win wavelength]*]

Options: [-dx value [-dy value] | -dz value] [-nodata value]

Advanced options: check table


This utility implements spatial and spectral filtering for raster data. In the spatial domain (X, Y), the filter typically involves a rectangular convolution kernel (moving window). To avoid image shifting, the size of the window should be odd (3, 5, 7, ...). You can set the window sizes in X and Y directions separately with the options -dx and -dy. A circular kernel (disc) is applied if option -circ is set. An overview of the supported filters (option -f|–filter) is given below. You can create customized filters by defining your own filter taps (multiplicative elements of the filter kernel) via an ascii file (option -tap). In the spectral/temporal domain (Z) you can filter multi-band raster inputs. The kernel filter size can be set with the option -dz (use odd values only).

Filters in spatial (dx, dy) and spectral/temporal (dz) domain

Implemented as moving window: choose dx, dy or dz > 1 and odd (3, 5, 7, etc.)

The number of output bands equals number of input bands

dilatemorphological dilation
erodemorphological erosion
closemorpholigical closing (dilate+erode)
openmorpholigical opening (erode+dilate)
smoothnodata valuessmooth nodata values (set nodata option!)

Example: "Smooth" (interpolate) nodata in spectral/temporal domain (-dz 1), using a linear interpolation

pkfilter -i input.tif -o smoothed.tif -dz 1 -f smoothnodata -interp linear

Example: Filter input.tif in spatial domain with morphological dilation filter with kernel size 3x3.

pkfilter -i input.tif -o dilated.tif -dx 3 -dy 3 -f dilate

Implemented as either moving window or statistical function in spectral/temporal domain (choose dz=1).

In case of moving window, the number of output bands equals number of input bands. In case dz=1, the single output band is calculated as the result of the statistical function applied to all bands.

filter description
nvalid report number of valid (not nodata) values in window
median perform a median filter in spatial (dx, dy) or spectral/temporal (dz) domain
var calculate variance in window
min calculate minimum in window
max calculate maximum in window
sum calculate sum in window
mean calculate mean in window
stdev calculate standard deviation in window
savgolay Savitzky-Golay filter (check examples page!)
percentile calculate percentile value in window
proportion calculate proportion in windoww

Example: Median filter in spatial domain

pkfilter -i input.tif -o median.tif -dx 3 -dy 3 -f median

Example: Calculate statistical variance in spectral/temporal domain (single output band)

pkfilter -i input.tif -o var.tif -dz 1 -f var

Wavelet filters

Wavelet filter in in spatial or spectral/temporal (set dz = 1) domain.

The number of output bands equals number of input bands

filter description
dwt discrete wavelet transform
dwti discrete inverse wavelet transform
dwt_cut discrete wavelet + inverse transform, using threshold option to cut percentile of coefficients

Example: Calculate discrete wavelet in spatial domain

pkfilter -i lena.tif -o lena_dwt.tif -f dwt

Example: Calculate discrete wavelet in spectral/temporal domain

pkfilter -i timeseries.tif -o dwt.tif -f dwt -dz 1

Wavelet filter implemented in spectral/temporal domain only.

The number of output bands equals number of input bands

filter description
dwt_cut_from discrete wavelet + inverse transform, setting all high frequence coefficients to zero (scale >= threshold)

Example: Calculate low frequency time series based on discrete wavelet + inverse transform in spectral/temporal domain, retaining only coefficients until scale 3

pkfilter -i timeseries.tif -o lowfrequency.tif -f dwt_cut_from -dz 1 -t 4

Filters in spatial domain only (dx, dy > 1 and odd).

The number of output bands equals number of input bands.

filter description
mrf Markov random field
ismin pixel is minimum?
ismax pixel is maximum?
shift perform a pixel shift in spatial window
scramble scramble pixels in a spatial window
mode (majority voting) perform a majority voring (set class option)
sobelx horizontal edge detection
sobely vertical edge detection
sobelxy diagonal edge detection (NE-SW)
sobelyx diagonal edge detection (NW-SE)
countid count digital numbers in window
order rank pixels in order
density calculated the density
homog central pixel must be identical to all other pixels within window
heterog central pixel must be different than all other pixels within window
sauvola Sauvola's thresholding method

Example: Sobel edge detection in horizontal direction

pkfilter -i lena.tif -o sobelx.tif -f sobelx -dx 5 -dy 5



Some examples how to use pkfilter can be found here