pkdiff

program to compare two raster image files

SYNOPSIS

Usage: pkdiff -i input -ref reference

Options: [-ln layer] [-b band] [-cm] [-lr attribute] [-c name -r value]* [-nodata value]* [-m mask] [-msknodata value]*

Advanced options: [-o output] [-f OGR format] [-lc attribute] [-bnd value [-hom] [-circ]] [-ct colortable] [-co NAME=VALUE]*

Description

The utility pkdiff compares two datasets. The reference can either be a raster or a vector, but the input must be a raster dataset. In case the reference is a raster dataset, a pixel by pixel comparison is performed. With no further options, the utility reports if the rasters are identical or different. If required, an output raster dataset can be written with a qualitative information per pixel: 0 (input=reference), 1 (input>reference) or 2 (input<reference). If, however, the reference is a vector dataset, it must consist of point features. Polygon features are automatically converted to the centroid points before analyzing.

A typical use of the utility is to assess the accuracy of an input raster land cover map, based on a reference vector dataset. The reference dataset must contain an attribute (label) for each class. A confusion matrix is produced if the option -cm|–confusion is set. Here too, an output dataset can be written, which will be a vector dataset in this case. It contains the reference feature points with the extracted data value of the raster input dataset as a new attribute.

Options

• use either -short or --long options (both --long=value and --long value are supported)
• short option -h shows basic options only, long option --help shows all options

  short	long	type	default	description
  i	input	std::string		Input raster dataset.
  ref	reference	std::string		Reference (raster or vector) dataset
  ln	ln	std::string		Layer name(s) in sample. Leave empty to select all (for vector reference datasets only)
  b	band	short	0	Input (reference) raster band. Optionally, you can define different bands for input and reference bands respectively: -b 1 -b 0.
  rmse	rmse	bool	false	Report root mean squared error
  reg	reg	bool	false	Report linear regression (Input = c0+c1*Reference)
  cm	confusion	bool	false	Create confusion matrix (to std out)
  lr	lref	std::string	label	Attribute name of the reference label (for vector reference datasets only)
  c	class	std::string		List of class names.
  r	reclass	short		List of class values (use same order as in classname option).
  nodata	nodata	double		No data value(s) in input or reference dataset are ignored
  m	mask	std::string		Use the first band of the specified file as a validity mask. Nodata values can be set with the option msknodata.
  msknodata	msknodata	double	0	Mask value(s) where image is invalid. Use negative value for valid data (example: use -t -1: if only -1 is valid value)
  o	output	std::string		Output dataset (optional)
  f	f	std::string	SQLite	OGR format for output vector (for vector reference datasets only)
  of	oformat	std::string	GTiff	Output image format (see also gdal_translate).
  lc	lclass	std::string	class	Attribute name of the classified label (for vector reference datasets only)
  cmf	cmf	std::string	ascii	Format for confusion matrix (ascii or latex)
  cmo	cmo	std::string		Output file for confusion matrix
  se95	se95	bool	false	Report standard error for 95 confidence interval
  bnd	boundary	short	1	Boundary for selecting the sample (for vector reference datasets only)
  hom	homogeneous	bool	false	Only take regions with homogeneous boundary into account (for reference datasets only)
  circ	circular	bool	false	Use circular boundary (for vector reference datasets only)
  ct	ct	std::string		Color table in ASCII format having 5 columns: id R G B ALFA (0: transparent, 255: solid).
  co	co	std::string		Creation option for output file. Multiple options can be specified.
  	commission	short	2	Value for commission errors: input label < reference label

  Usage: pkdiff -i input -ref reference

Examples

Some examples how to use pkdiff can be found here