bionic (1) otbcli_TrainVectorClassifier.1.gz
NAME
otbcli_TrainVectorClassifier - OTB TrainVectorClassifier application
DESCRIPTION
This is the TrainVectorClassifier application, version 5.6.0 Train a classifier based on labeled
geometries and a list of features to consider.
Complete documentation: http://www.orfeo-toolbox.org/Applications/TrainVectorClassifier.html
Parameters:
-progress <boolean>
Report progress
-io.stats <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)
-feat <string list> Field names for training features. (mandatory, default value is
)
-cfield <string> Field containing the class id for supervision (mandatory,
default value is class)
-layer <int32> Layer Index (optional, on by default, default value is 0)
-valid.vd <string> Validation Vector Data (optional, off by default)
-valid.layer <int32> Layer Index (optional, on by default, default value is 0)
-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_TrainVectorClassifier -io.vd vectorData.shp -io.stats meanVar.xml -io.out svmModel.svm -feat
perimeter area width -cfield predicted