NAME

       pksvm - classify raster image using Support Vector Machine

SYNOPSIS

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

DESCRIPTION

       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

       -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
              --bagsize options, where a random subset is taken from a single training file)

       -i filename, --input filename
              input image

       -o filename, --output filename
              Output classification image

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

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

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

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

       -of GDALformat, --oformat GDALformat
              Output image format (see also gdal_translate(1)).

       -f format, --f format
              Output ogr format for active training sample

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

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

       -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) Used
              for input only (ignored for cross validation)

       -g gamma, --gamma gamma
              Gamma in kernel function

       -cc cost, --ccost cost
              The parameter C of C_SVC, epsilon_SVR, and nu_SVR

       -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.

       -nodata value, --nodata value
              Nodata value to put where image is masked as nodata

       -v level, --verbose level
              Verbose level

       Advanced options

       -b band, --band band
              Band index (starting from 0,  either  use  --band  option  or  use  --startband  to
              --endband)

       -sband band, --startband band
              Start band sequence number

       -eband band, --endband band
              End band sequence number

       -bal size, --balance size
              Balance the input data to this number of samples for each class

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

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

       -bagsize value, --bagsize 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

       -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.

       -cb filename, --classbag filename
              Output for each individual bootstrap aggregation

       -prob filename, --prob filename
              Probability image.

       -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]  (use  0 if scale min and max in each band
              to -1.0 and 1.0)

       -svmt type, --svmtype type
              Type of SVM (C_SVC, nu_SVC,one_class, epsilon_SVR, nu_SVR)

       -kt type, --kerneltype type
              Type of kernel function (linear,polynomial,radial,sigmoid)

       -kd value, --kd value
              Degree in kernel function

       -c0 value, --coef0 value
              Coef0 in kernel function

       -nu value, --nu value
              The parameter nu of nu-SVC, one-class SVM, and nu-SVR

       -eloss value, --eloss value
              The epsilon in loss function of epsilon-SVR

       -cache number, --cache number
              Cache ⟨http://pktools.nongnu.org/html/classCache.html⟩ memory size in MB  (default:
              100)

       -etol value, --etol value
              the tolerance of termination criterion (default: 0.001)

       -shrink, --shrink
              Whether to use the shrinking heuristics

       -na number, --nactive number
              Number of active training points

EXAMPLE

       Classify  input  image input.tif with a support vector machine.  A training sample that is
       provided as an OGR vector dataset.  It  contains  all  features  (same  dimensionality  as
       input.tif)  in  its  fields (please check pkextract(1) on how to obtain such a file from a
       "clean" vector file containing locations only).   A  two-fold  cross  validation  (cv)  is
       performed (output on screen).  The parameters cost and gamma of the support vector machine
       are set to 1000 and 0.1 respectively.  A colourtable  (a  five  column  text  file:  image
       value, RED, GREEN, BLUE, ALPHA) has also been provided.

       pksvm -i input.tif -t training.sqlite -o output.tif -cv 2 -ct colourtable.txt -cc 1000 -g 0.1

       Classification  using  bootstrap  aggregation.   The  training sample is randomly split in
       three subsamples (33% of the original sample each).

       pksvm -i input.tif -t training.sqlite -o output.tif -bs 33 -bag 3

       Classification using prior probabilities for each class.   The  priors  are  automatically
       normalized.  The order in which the options -p are provide should respect the alphanumeric
       order of the class names (class 10 comes before 2...)

       pksvm -i input.tif -t training.sqlite -o output.tif -p 1 -p 1 -p 1 -p 1 -p 1 -p 1 -p 1 -p 1 -p 1 -p 1 -p 1 -p 0.2 -p 1 -p 1 -p 1

                                         24 January 2016                                 pksvm(1)