Provided by: otb-bin_6.6.1+dfsg-1build1_amd64 bug

NAME

       otbcli_TrainImagesClassifier - OTB TrainImagesClassifier application

DESCRIPTION

       This  is  the  TrainImagesClassifier  application,  version  5.2.0 Train a classifier from
       multiple pairs of images and training vector data.

       Complete                                                                    documentation:
       http://www.orfeo-toolbox.org/Applications/TrainImagesClassifier.html

   Parameters:
       -progress
              <boolean>        Report progress

        -io.il                 <string list>    Input Image List  (mandatory)
        -io.vd                 <string list>    Input Vector Data List  (mandatory)

       -io.imstat
              <string>         Input XML image statistics file  (optional, off by default)

       -io.confmatout
              <string>         Output confusion matrix  (optional, off by default)

        -io.out                <string>         Output model  (mandatory)

       -elev.dem
              <string>         DEM directory  (optional, off by default)

       -elev.geoid
              <string>         Geoid File  (optional, off by default)

       -elev.default
              <float>          Default elevation  (mandatory, default value is 0)

       -sample.mt
              <int32>           Maximum training sample size per class  (mandatory, default value
              is 1000)

       -sample.mv
              <int32>          Maximum validation sample  size  per  class   (mandatory,  default
              value is 1000)

       -sample.bm
              <int32>          Bound sample number by minimum  (mandatory, default value is 1)

       -sample.edg
              <boolean>        On edge pixel inclusion  (optional, off by default)

       -sample.vtr
              <float>          Training and validation sample ratio  (mandatory, default value is
              0.5)

       -sample.vfn
              <string>         Name of the discrimination field   (mandatory,  default  value  is
              Class)

       -classifier
              <string>         Classifier to use for the training [boost/dt/gbt/ann/bayes/rf/knn]
              (mandatory, default value is boost)

       -classifier.boost.t
              <string>         Boost Type [discrete/real/logit/gentle] (mandatory, default  value
              is real)

       -classifier.boost.w
              <int32>          Weak count  (mandatory, default value is 100)

       -classifier.boost.r
              <float>          Weight Trim Rate  (mandatory, default value is 0.95)

       -classifier.boost.m
              <int32>          Maximum depth of the tree  (mandatory, default value is 1)

       -classifier.dt.max
              <int32>          Maximum depth of the tree  (mandatory, default value is 65535)

       -classifier.dt.min
              <int32>           Minimum number of samples in each node  (mandatory, default value
              is 10)

       -classifier.dt.ra
              <float>          Termination criteria  for  regression  tree   (mandatory,  default
              value is 0.01)

       -classifier.dt.cat
              <int32>           Cluster  possible  values of a categorical variable into K <= cat
              clusters to find a suboptimal split  (mandatory, default value is 10)

       -classifier.dt.f
              <int32>          K-fold cross-validations  (mandatory, default value is 10)

       -classifier.dt.r
              <boolean>        Set Use1seRule flag to false  (optional, off by default)

       -classifier.dt.t
              <boolean>        Set TruncatePrunedTree flag to false  (optional, off by default)

       -classifier.gbt.w
              <int32>          Number of boosting algorithm iterations  (mandatory, default value
              is 200)

       -classifier.gbt.s
              <float>          Regularization parameter  (mandatory, default value is 0.01)

       -classifier.gbt.p
              <float>           Portion  of  the  whole  training  set  used  for  each algorithm
              iteration  (mandatory, default value is 0.8)

       -classifier.gbt.max
              <int32>          Maximum depth of the tree  (mandatory, default value is 3)

       -classifier.ann.t
              <string>         Train Method Type [reg/back] (mandatory, default value is reg)

       -classifier.ann.sizes
              <string list>    Number of neurons in each intermediate layer  (mandatory)

       -classifier.ann.f
              <string>          Neuron  activation  function  type  [ident/sig/gau]   (mandatory,
              default value is sig)

       -classifier.ann.a
              <float>           Alpha  parameter  of the activation function  (mandatory, default
              value is 1)

       -classifier.ann.b
              <float>          Beta parameter of the  activation  function   (mandatory,  default
              value is 1)

       -classifier.ann.bpdw
              <float>           Strength  of  the  weight  gradient  term  in the BACKPROP method
              (mandatory, default value is 0.1)

       -classifier.ann.bpms
              <float>          Strength of the momentum term (the difference between  weights  on
              the 2 previous iterations)  (mandatory, default value is 0.1)

       -classifier.ann.rdw
              <float>           Initial value Delta_0 of update-values Delta_{ij} in RPROP method
              (mandatory, default value is 0.1)

       -classifier.ann.rdwm
              <float>          Update-values lower limit Delta_{min} in RPROP method  (mandatory,
              default value is 1e-07)

       -classifier.ann.term
              <string>          Termination  criteria [iter/eps/all] (mandatory, default value is
              all)

       -classifier.ann.eps
              <float>          Epsilon  value  used  in  the  Termination  criteria   (mandatory,
              default value is 0.01)

       -classifier.ann.iter
              <int32>           Maximum  number  of  iterations  used in the Termination criteria
              (mandatory, default value is 1000)

       -classifier.rf.max
              <int32>          Maximum depth of the tree  (mandatory, default value is 5)

       -classifier.rf.min
              <int32>          Minimum number of samples in each node  (mandatory, default  value
              is 10)

       -classifier.rf.ra
              <float>           Termination  Criteria  for  regression  tree  (mandatory, default
              value is 0)

       -classifier.rf.cat
              <int32>          Cluster possible values of a categorical variable into  K  <=  cat
              clusters to find a suboptimal split  (mandatory, default value is 10)

       -classifier.rf.var
              <int32>          Size of the randomly selected subset of features at each tree node
              (mandatory, default value is 0)

       -classifier.rf.nbtrees <int32>
              Maximum number of trees in the forest  (mandatory, default value is 100)

       -classifier.rf.acc
              <float>          Sufficient accuracy (OOB  error)   (mandatory,  default  value  is
              0.01)

       -classifier.knn.k
              <int32>          Number of Neighbors  (mandatory, default value is 32)

       -rand  <int32>          set user defined seed  (optional, off by default)

       -inxml <string>         Load otb application from xml file  (optional, off by default)

EXAMPLES

       otbcli_TrainImagesClassifier  -io.il  QB_1_ortho.tif  -io.vd VectorData_QB1.shp -io.imstat
       EstimateImageStatisticsQB1.xml -sample.mv 100 -sample.mt 100 -sample.vtr  0.5  -sample.edg
       false     -sample.vfn     Class    -classifier    libsvm    -classifier.libsvm.k    linear
       -classifier.libsvm.c 1 -classifier.libsvm.opt false -io.out svmModelQB1.txt -io.confmatout
       svmConfusionMatrixQB1.csv

SEE ALSO

       The full documentation for otbcli_TrainImagesClassifier is maintained as a Texinfo manual.
       If the info and otbcli_TrainImagesClassifier programs are properly installed at your site,
       the command

              info otbcli_TrainImagesClassifier

       should give you access to the complete manual.