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)