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, 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(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 subset 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 image.  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  neuron 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).   Also 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)

                                           30 July 2024                               pkannogr(1)