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pkfilter(1) pkfilter(1)

NAME

pkfilter - program to filter raster images

SYNOPSIS


pkfilter
-i input -o output [-f filter | -srf file [-srf file]... | -fwhm value [-fwhm value]... ] [options] [advanced options]

DESCRIPTION

pkfilter 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

The number of output bands equals number of input bands

Filter Description
dilate morphological dilation
erode morphological erosion
close morpholigical closing (dilate+erode)
open morpholigical opening (erode+dilate)
smoothnodata values smooth 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

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 window

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

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

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 solbelx -dx 5 -dy 5

OPTIONS

input image
output classification image
filter function (nvalid, median, var, min, max, sum, mean, dilate, erode, close, open, homog (central pixel must be identical to all other pixels within window), heterog (central pixel must be different than all other pixels within window), sobelx (horizontal edge detection), sobely (vertical edge detection), sobelxy (diagonal edge detection NE-SW),sobelyx (diagonal edge detection NW-SE), density, countid, mode (majority voting, only for classes), smoothnodata (smooth nodata values only) values, ismin, ismax, order (rank pixels in order), stdev, mrf, dwt, dwti, dwt_cut, dwt_cut_from, scramble, shift, savgolay, percentile, proportion)
filter kernel size in x, use odd values only
filter kernel size in y, use odd values only
filter kernel size in z (band or spectral dimension), must be odd (example: 3). Set dz>0 if 1-D filter must be used in band domain
nodata value(s) (used for smoothnodata filter)
verbose mode if > 0

Advanced options

circular disc kernel for dilation and erosion
Resampling method for shifting operation (near: nearest neighbour, bilinear: bi-linear interpolation).
Creation option for output file. Multiple options can be specified.
wavelet type: daubechies,daubechies_centered, haar, haar_centered, bspline, bspline_centered
wavelet family (vanishing moment, see also http://www.gnu.org/software/gsl/manual/html_node/DWT-Initialization.html)
class value(s) to use for density, erosion, dilation, openening and closing, thresholding
threshold value(s) to use for threshold filter (one for each class), or threshold to cut for dwt_cut (use 0 to keep all) or dwt_cut_from, or sigma for shift
text file containing taps used for spatial filtering (from ul to lr). Use dimX and dimY to specify tap dimensions in x and y. Leave empty for not using taps
taps used for spectral filtering
Padding method for filtering (how to handle edge effects). Choose between: symmetric, replicate, circular, constant (pad with 0).
list of full width half to apply spectral filtering (-fwhm band1 -fwhm band2 ...)
list of ASCII files containing spectral response functions (two columns: wavelength response)
column number of input ASCII file containing wavelengths
list of wavelengths in output spectrum (-wout band1 -wout band2 ...)
down sampling factor. Use value 1 for no downsampling). Use value n>1 for downsampling (aggregation)
ASCII file with beta for each class transition in Markov Random Field
error marging for linear feature
obtain longest object length for linear feature
obtain angle found for longest object length for linear feature
obtain angle found for shortest object length for linear feature
type of interpolation for spectral filtering (see http://www.gnu.org/software/gsl/manual/html_node/Interpolation-Types.html)
Data type for output image ({Byte / Int16 / UInt16 / UInt32 / Int32 / Float32 / Float64 / CInt16 / CInt32 / CFloat32 / CFloat64}). Empty string: inherit type from input image
Output image format (see also gdal_translate(1)).
color table (file with 5 columns: id R G B ALFA (0: transparent, 255: solid)). Use none to omit color table

EXAMPLE

Filtering in spatial domain

Filter input.tif with morphological dilation filter. Use a circular kernel (instead of rectangular) of size 3x3.

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

Similar to previous example, but consider only values of 255 for filtering operation. Typical usage: dilate cloud values in input image that are flagged as 255

pkfilter -i input.tif -o filter.tif -dx 3 -dy 3 -class 255 -f dilate -circ

Filtering in spectral/temporal domain

Calculate the median value for each pixel, calculated on a moving window of width 3 (-dz 3) over all input bands. The output raster dataset will contain as many bands as the input raster dataset.

pkfilter -i input.tif -o filter_stdev.tif -dz 3 -f median

No moving window (-dz 1). Calculate the standard deviation for each pixel, calculated on all input bands. The output raster dataset will contain a single band only.

pkfilter -i input.tif -o filter_stdev.tif -dz 1 -f stdev

"Smooth" (interpolate) nodata in spectral/temporal domain (-dz 1), using a linear interpolation. The following interpolation types are supported: akima (default), linear, polynomial, cspline, cspline_periodic, akima_periodic (please check gsl ⟨http://www.gnu.org/software/gsl/manual/html_node/Interpolation-Types.html⟩ page for more information on the interpolation types).

pkfilter -i input.tif -o input_smoothed.tif -dz 1 -f smoothnodata -interp linear
06 December 2020