NAME

       pkannogr - classify vector dataset using Artificial Neural Network

SYNOPSIS

       pkannogr -t training [-i input] [-o output] [-cv value] [options] [advanced options]

DESCRIPTION

       pkannogr implements an artificial neural network (ANN) to solve a supervised classification problem.  The
       implementation  is  based  on  the open source C++ library ( fann ⟨http://leenissen.dk/fann/wp/⟩ ).  Both
       raster and vector files are supported as input.  The output will contain the classification  result,  ei‐
       ther  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 sam‐
       ple for classifying different images, provided the number of bands of the images are identical.  Use  the
       utility  pkextract(1) 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

       -i filename, --input filename
              input image

       -t filename, --training filename
              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 sub‐
              set is taken from a single training file)

       -tln layer, --tln layer
              training layer name(s)

       -label attribute, --label attribute
              identifier for class label in training vector file.  (default: label)

       -prior value, --prior value
              prior probabilities for each class (e.g., -prior 0.3 -prior 0.3 -prior 0.2 )

       -cv value, --cv value
              n-fold cross validation mode (default: 0)

       -nn number, --nneuron number
              number of neurons in hidden layers in neural network (multiple hidden layers are set  by  defining
              multiple number of neurons: -nn 15 -nn 1, default is one hidden layer with 5 neurons)

       -m filename, --mask filename
              Only  classify  within specified mask (vector or raster).  For raster mask, set nodata values with
              the option --msknodata.

       -msknodata value, --msknodata value
              mask value(s) not to consider for classification.  Values will be taken over in classification im‐
              age.  Default is 0.

       -nodata value, --nodata value
              nodata value to put where image is masked as nodata (default: 0)

       -o filename, --output filename
              output classification image

       -ot type, --otype type
              Data type for output image ({Byte / Int16 / UInt16 / UInt32 / Int32 / Float32 / Float64 / CInt16 /
              CInt32 / CFloat32 / CFloat64}).  Empty string: inherit type from input image

       -of GDALformat, --oformat GDALformat
              Output image format (see also gdal_translate(1)).  Empty string: inherit from input image

       -f OGRformat, --f OGRformat
              Output ogr format for active training sample (default: SQLite)

       -ct filename, --ct filename
              colour table in ASCII format having 5 columns: id R G B ALFA (0: transparent, 255: solid)

       -co NAME=VALUE, --co NAME=VALUE
              Creation option for output file.  Multiple options can be specified.

       -c name, --class name
              list of class names.

       -r value, --reclass value
              list of class values (use same order as in --class option).

       -v 0|1|2, --verbose 0|1|2
              set to: 0 (results only), 1 (confusion matrix), 2 (debug)

       Advanced options

       -bal size, --balance size
              balance the input data to this number of samples for each class (default: 0)

       -min number, --min number
              if number of training pixels is less then min, do not take this class into  account  (0:  consider
              all classes)

       -b band, --band band
              band index (starting from 0, either use --band option or use --start to --end)

       -sband band, --startband band
              start band sequence number (default: 0)

       -eband band, --endband band
              end band sequence number

       -offset value, --offset value
              offset value for each spectral band input features: refl[band]=(DN[band]-offset[band])/scale[band]

       -scale value, --scale value
              scale value for each spectral band input features: refl=(DN[band]-offset[band])/scale[band]

       -a 1|2, --aggreg 1|2
              how to combine aggregated classifiers, see also --rc option (1: sum rule, 2: max rule).

       --connection 0|1
              connection rate (default: 1.0 for a fully connected network)

       -w weights, --weights weights
              weights for neural network.  Apply to fully connected network only, starting from first input neu‐
              ron to last output neuron, including the bias neurons (last neuron in each but last layer)

       -l rate, --learning rate
              learning rate (default: 0.7)

       --maxit number
              number of maximum iterations (epoch) (default: 500)

       -comb rule, --comb rule
              how to combine bootstrap aggregation classifiers (0: sum rule, 1: product rule, 2: max rule).  Al‐
              so used to aggregate classes with --rc option.  Default is sum rule (0)

       -bag value, --bag value
              Number of bootstrap aggregations (default is no bagging: 1)

       -bs value, --bsize value
              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. default: 100)

       -cb filename, --classbag filename
              output for each individual bootstrap aggregation (default is blank)

       --prob filename
              probability image.  Default is no probability image

       -na number, --na number
              number of active training points (default: 1)

                                                  01 April 2024                                      pkannogr(1)