OTBCLI_TRAINIMAGESCLASSIFIER(1)

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.