pkextract

pkextract

extract pixel values from raster image from a (vector or raster) sample

SYNOPSIS

Usage: pkextract -i input [-s sample | -rand number | -grid size] -o output

Options: [-ln layer]* [-c class]* [-t threshold]* [-f format] [-ft fieldType] [-lt labelType] [-polygon] [-b band]* [-r rule]

Advanced options: [-bndnodata band -srcnodata value]* [-tp threshold] [-test testSample] [-bn attribute] [-cn attribute] [-geo value] [-down value] [-buf value [-circ]]

Description

The utility pkextract extracts pixel values from an input raster dataset, based on the locations you provide via a sample file. Alternatively, a random sample or systematic grid of points can also be extracted. The sample can be a vector file with points or polygons. In the case of polygons, you can either extract the values for all raster pixels that are covered by the polygons, or extract a single value for each polygon such as the centroid, mean, median, etc. As output, a new copy of the vector file is created with an extra attribute for the extracted pixel value. For each raster band in the input image, a separate attribute is created. For instance, if the raster dataset contains three bands, three attributes are created (b0, b1 and b2).

Instead of a vector dataset, the sample can also be a raster dataset with categorical values. The typical use case is a land cover map that overlaps the input raster dataset. The utility then extracts pixels from the input raster for the respective land cover classes. To select a random subset of the sample raster dataset you can set the threshold option -t with a percentage value.

A typical usage of pkextract is to prepare a training sample for one of the classifiers implemented in pktools.

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		Raster input dataset containing band information
  s	sample	std::string		OGR vector dataset with features to be extracted from input data. Output will contain features with input band information included. Sample image can also be GDAL raster dataset.
  ln	ln	std::string		Layer name(s) in sample (leave empty to select all)
  rand	random	unsigned int		Create simple random sample of points. Provide number of points to generate
  grid	grid	double		Create systematic grid of points. Provide cell grid size (in projected units, e.g,. m)
  o	output	std::string		Output sample dataset
  c	class	int		Class(es) to extract from input sample image. Leave empty to extract all valid data pixels from sample dataset. Make sure to set classes if rule is set to mode or proportion
  t	threshold	float	100	Probability threshold for selecting samples (randomly). Provide probability in percentage (>0) or absolute (<0). Use a single threshold for vector sample datasets. If using raster land cover maps as a sample dataset, you can provide a threshold value for each class (e.g. -t 80 -t 60). Use value 100 to select all pixels for selected class(es)
  f	f	std::string	SQLite	Output sample dataset format
  ft	ftype	std::string	Real	Field type (only Real or Integer)
  lt	ltype	std::string	Integer	Label type: In16 or String
  polygon	polygon	bool	false	Create OGRPolygon as geometry instead of OGRPoint.
  b	band	int		Band index(es) to extract (0 based). Leave empty to use all bands
  r	rule	std::string	centroid	Rule how to report image information per feature (only for vector sample). point (value at each point or at centroid if polygon), centroid, mean, stdev, median, proportion, min, max, mode, sum.
  bndnodata	bndnodata	int	0	Band(s) in input image to check if pixel is valid (used for srcnodata)
  srcnodata	srcnodata	double		Invalid value(s) for input image
  tp	thresholdPolygon	float		(absolute) threshold for selecting samples in each polygon
  test	test	std::string		Test sample dataset (use this option in combination with threshold<100 to create a training (output) and test set
  bn	bname	std::string	b	For single band input data, this extra attribute name will correspond to the raster values. For multi-band input data, multiple attributes with this prefix will be added (e.g. b0, b1, b2, etc.)
  cn	cname	std::string	label	Name of the class label in the output vector dataset
  geo	geo	short	1	Use geo coordinates (set to 0 to use image coordinates)
  down	down	short	1	Down sampling factor (for raster sample datasets only). Can be used to create grid points
  buf	buffer	short		Buffer for calculating statistics for point features
  circ	circular	bool	false	Use a circular disc kernel buffer (for vector point sample datasets only, use in combination with buffer option)

  Usage: pkextract -i input [-s sample | -rand number | -grid size] -o output

Examples

Some examples how to use pkextract can be found here