pkann

classify raster image using Artificial Neural Network

SYNOPSIS

Usage: pkann -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]* [-nn number]* [-m filename [-msknodata value]*] [-nodata value]

Advanced options: [-b band] [-sband band -eband band]* [-bal size]* [-min] [-bag value] [-bs value] [-comb rule] [-cb filename] [-prob filename] [-pim priorimage] [–offset value] [–scale value] [–connection 0|1] [-w weights]* [–learning rate] [–maxit number]

Description

The utility pkann implements an artificial neural network (ANN) to solve a supervised classification problem. The implementation is based on the open source C++ library fann). 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
  i	input	std::string		input image
  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)
  tln	tln	std::string		training layer name(s)
  label	label	std::string	label	identifier for class label in training vector file.
  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)
  b	band	short		band index (starting from 0, either use band option or use start to end)
  sband	startband	unsigned short		Start band sequence number
  eband	endband	unsigned short		End band sequence number
  	offset	double	0	offset value for each spectral band input features: refl[band]=(DN[band]-offset[band])/scale[band]
  scale	scale	double	0	scale value for each spectral band input features: refl=(DN[band]-offset[band])/scaleband
  a	aggreg	unsigned short	1	how to combine aggregated classifiers, see also rc option (1: sum rule, 2: max rule).
  prior	prior	double	0	prior probabilities for each class (e.g., -p 0.3 -p 0.3 -p 0.2 )
  pim	priorimg	std::string		prior probability image (multi-band img with band for each class
  cv	cv	unsigned short	0	n-fold cross validation mode
  cmf	cmf	std::string	ascii	Format for confusion matrix (ascii or latex)
  nn	nneuron	unsigned int	5	number of neurons in hidden layers in neural network (multiple hidden layers are set by defining multiple number of neurons: -n 15 -n 1, default is one hidden layer with 5 neurons)
  	connection	float	1	connection reate (default: 1.0 for a fully connected network)
  w	weights	float	0	weights for neural network. Apply to fully connected network only, starting from first input neuron to last output neuron, including the bias neurons (last neuron in each but last layer)
  l	learning	float	0.7	learning rate (default: 0.7)
  	maxit	unsigned int	500	number of maximum iterations (epoch) (default: 500)
  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. Default is sum rule (0)
  bag	bag	unsigned short	1	Number of bootstrap aggregations (default is no bagging: 1)
  bs	bsize	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
  cb	classbag	std::string		output for each individual bootstrap aggregation (default is blank)
  m	mask	std::string		Only classify within specified mask (vector or raster). For raster mask, set nodata values with the option msknodata.
  msknodata	msknodata	short	0	mask value(s) not to consider for classification. Values will be taken over in classification image. Default is 0
  nodata	nodata	unsigned short	0	nodata value to put where image is masked as nodata
  o	output	std::string		output classification image
  ot	otype	std::string		Data type for output image ({Byte/Int16/UInt16/UInt32/Int32/Float32/Float64/CInt16/CInt32/CFloat32/CFloat64}). Empty string: inherit type from input image
  of	oformat	std::string		Output image format (see also gdal_translate). Empty string: inherit from input image
  ct	ct	std::string		colour 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.
  	prob	std::string		probability image. Default is no probability image
  f	f	std::string	SQLite	Output ogr format for active training sample
  na	nactive	unsigned int	1	number of active training points
  c	class	std::string		list of class names.
  r	reclass	short		list of class values (use same order as in class opt).

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

Examples

Some examples how to use pkann can be found here