NAME

       pksvmogr - classify vector dataset using Support Vector Machine

SYNOPSIS

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

DESCRIPTION

       pksvmogr  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

                                                10 February 2020                                     pksvmogr(1)