pksvm

pksvm

classify raster image using Support Vector Machine

SYNOPSIS

Usage: pksvm -t training [-i input -o output] [-cv value]

Options: [-tln layer]* [-c name -r value]* [-of GDALformat|-f OGRformat] [-co NAME=VALUE]* [-ct filename] [-label attribute] [-prior value]* [-g gamma] [-cc cost] [-m filename [-msknodata value]*] [-nodata value]

Advanced options: [-b band] [-s band] [-e band] [-bal size]* [-min] [-bag value] [-bs value] [-comb rule] [-cb filename] [-prob filename] [-pim priorimage] [–offset value] [–scale value] [-svmt type] [-kt type] [-kd value] [-c0 value] [-nu value] [-eloss value] [-cache value] [-etol value] [-shrink]

Description

The utility pksvm implements a support vector machine (SVM) to solve a supervised classification problem. The implementation is based on the open source C++ library libSVM (http://www.csie.ntu.edu.tw/~cjlin/libsvm). Both raster and vector files are supported as input. The output will contain the classification result, either in raster or vector format, corresponding to the format of the input. A training sample must be provided as an OGR vector dataset that contains the class labels and the features for each training point. The point locations are not considered in the training step. You can use the same training sample for classifying different images, provided the number of bands of the images are identical. Use the utility pkextract to create a suitable training sample, based on a sample of points or polygons. For raster output maps you can attach a color table using the option -ct.

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
  t	training	std::string		Training vector file. A single vector file contains all training features (must be set as: b0, b1, b2,...) for all classes (class numbers identified by label option). Use multiple training files for bootstrap aggregation (alternative to the bag and bsize options, where a random subset is taken from a single training file)
  i	input	std::string		input image
  o	output	std::string		Output classification image
  cv	cv	unsigned short	0	N-fold cross validation mode
  tln	tln	std::string		Training layer name(s)
  c	class	std::string		List of class names.
  r	reclass	short		List of class values (use same order as in class opt).
  of	oformat	std::string		Output image format (see also gdal_translate). Empty string: inherit from input image
  f	f	std::string	SQLite	Output ogr format for active training sample
  co	co	std::string		Creation option for output file. Multiple options can be specified.
  ct	ct	std::string		Color table in ASCII format having 5 columns: id R G B ALFA (0: transparent, 255: solid)
  label	label	std::string	label	Attribute name for class label in training vector file.
  prior	prior	double	0	Prior probabilities for each class (e.g., -p 0.3 -p 0.3 -p 0.2 ). Used for input only (ignored for cross validation)
  g	gamma	float	1	Gamma in kernel function
  cc	ccost	float	1000	The parameter C of C_SVC, epsilon_SVR, and nu_SVR
  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	short	0	Mask value(s) not to consider for classification (use negative values if only these values should be taken into account). Values will be taken over in classification image.
  nodata	nodata	unsigned short	0	Nodata value to put where image is masked as nodata
  b	band	short		Band index (starting from 0, either use band option or use start to end)
  s	start	double	0	Start band sequence number
  e	end	double	0	End band sequence number (set to 0 to include all bands)
  bal	balance	unsigned int	0	Balance the input data to this number of samples for each class
  min	min	int	0	If number of training pixels is less then min, do not take this class into account (0: consider all classes)
  bag	bag	unsigned short	1	Number of bootstrap aggregations
  bagsize	bagsize	int	100	Percentage of features used from available training features for each bootstrap aggregation (one size for all classes, or a different size for each class respectively
  comb	comb	unsigned short	0	How to combine bootstrap aggregation classifiers (0: sum rule, 1: product rule, 2: max rule). Also used to aggregate classes with rc option.
  cb	classbag	std::string		Output for each individual bootstrap aggregation
  prob	prob	std::string		Probability image.
  pim	priorimg	std::string		Prior probability image (multi-band img with band for each class
  	offset	double	0	Offset value for each spectral band input features: refl[band]=(DN[band]-offset[band])/scale[band]
  	scale	double	0	Scale value for each spectral band input features: refl=(DN[band]-offset[band])/scaleband
  svmt	svmtype	std::string	C_SVC	Type of SVM (C_SVC, nu_SVC,one_class, epsilon_SVR, nu_SVR)
  kt	kerneltype	std::string	radial	Type of kernel function (linear,polynomial,radial,sigmoid)
  kd	kd	unsigned short	3	Degree in kernel function
  c0	coef0	float	0	Coef0 in kernel function
  nu	nu	float	0.5	The parameter nu of nu_SVC, one_class SVM, and nu_SVR
  eloss	eloss	float	0.1	The epsilon in loss function of epsilon_SVR
  cache	cache	int	100	Cache memory size in MB
  etol	etol	float	0.001	The tolerance of termination criterion
  shrink	shrink	bool	false	Whether to use the shrinking heuristics
  pe	probest	bool	true	Whether to train a SVC or SVR model for probability estimates
  entropy	entropy	std::string		Entropy image (measure for uncertainty of classifier output
  active	active	std::string		Ogr output for active training sample.
  na	nactive	unsigned int	1	Number of active training points
  v	verbose	short	0	Verbose level
  random	random	bool	true	Randomize training data for balancing and bagging

  Usage: pksvm -t training [-i input -o output] [-cv value]

Examples

Some examples how to use pksvm can be found here