Provided by: otb-bin_6.6.1+dfsg-2_amd64 
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.