Train a classifier based on labeled geometries and a list of features to consider.
This application trains a classifier based on labeled geometries and a list of features to consider for classification.
This section describes in details the parameters available for this application. Table 4.140, page 773 presents a summary of these parameters and the parameters keys to be used in command-line and programming languages. Application key is TrainVectorClassifier.
Parameter key | Parameter type |
Parameter description |
io | Group |
Input and output data |
io.vd | Input vector data |
Input Vector Data |
io.stats | Input File name |
Input XML image statistics file |
io.confmatout | Output File name |
Output confusion matrix |
io.out | Output File name |
Output model |
feat | List |
Field names for training features. |
cfield | String |
Field containing the class id for supervision |
layer | Int |
Layer Index |
valid | Group |
Validation data |
valid.vd | Input vector data |
Validation Vector Data |
valid.layer | Int |
Layer Index |
classifier | Choices |
Classifier to use for the training |
classifier libsvm | Choice |
LibSVM classifier |
classifier boost | Choice |
Boost classifier |
classifier dt | Choice |
Decision Tree classifier |
classifier gbt | Choice |
Gradient Boosted Tree classifier |
classifier ann | Choice |
Artificial Neural Network classifier |
classifier bayes | Choice |
Normal Bayes classifier |
classifier rf | Choice |
Random forests classifier |
classifier knn | Choice |
KNN classifier |
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classifier.libsvm.k | Choices |
SVM Kernel Type |
classifier.libsvm.k linear | Choice |
Linear |
classifier.libsvm.k rbf | Choice |
Gaussian radial basis function |
classifier.libsvm.k poly | Choice |
Polynomial |
classifier.libsvm.k sigmoid | Choice |
Sigmoid |
classifier.libsvm.m | Choices |
SVM Model Type |
classifier.libsvm.m csvc | Choice |
C support vector classification |
classifier.libsvm.m nusvc | Choice |
Nu support vector classification |
classifier.libsvm.m oneclass | Choice |
Distribution estimation (One Class SVM) |
classifier.libsvm.c | Float |
Cost parameter C |
classifier.libsvm.opt | Boolean |
Parameters optimization |
classifier.libsvm.prob | Boolean |
Probability estimation |
classifier.boost.t | Choices |
Boost Type |
classifier.boost.t discrete | Choice |
Discrete AdaBoost |
classifier.boost.t real | Choice |
Real AdaBoost (technique using confidence-rated predictions and working well with categorical data) |
classifier.boost.t logit | Choice |
LogitBoost (technique producing good regression fits) |
classifier.boost.t gentle | Choice |
Gentle AdaBoost (technique setting less weight on outlier data points and, for that reason, being often good with regression data) |
classifier.boost.w | Int |
Weak count |
classifier.boost.r | Float |
Weight Trim Rate |
classifier.boost.m | Int |
Maximum depth of the tree |
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classifier.dt.max | Int |
Maximum depth of the tree |
classifier.dt.min | Int |
Minimum number of samples in each node |
classifier.dt.ra | Float |
Termination criteria for regression tree |
classifier.dt.cat | Int |
Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split |
classifier.dt.f | Int |
K-fold cross-validations |
classifier.dt.r | Boolean |
Set Use1seRule flag to false |
classifier.dt.t | Boolean |
Set TruncatePrunedTree flag to false |
classifier.gbt.w | Int |
Number of boosting algorithm iterations |
classifier.gbt.s | Float |
Regularization parameter |
classifier.gbt.p | Float |
Portion of the whole training set used for each algorithm iteration |
classifier.gbt.max | Int |
Maximum depth of the tree |
classifier.ann.t | Choices |
Train Method Type |
classifier.ann.t reg | Choice |
RPROP algorithm |
classifier.ann.t back | Choice |
Back-propagation algorithm |
classifier.ann.sizes | String list |
Number of neurons in each intermediate layer |
classifier.ann.f | Choices |
Neuron activation function type |
classifier.ann.f ident | Choice |
Identity function |
classifier.ann.f sig | Choice |
Symmetrical Sigmoid function |
classifier.ann.f gau | Choice |
Gaussian function (Not completely supported) |
classifier.ann.a | Float |
Alpha parameter of the activation function |
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classifier.ann.b | Float |
Beta parameter of the activation function |
classifier.ann.bpdw | Float |
Strength of the weight gradient term in the BACKPROP method |
classifier.ann.bpms | Float |
Strength of the momentum term (the difference between weights on the 2 previous iterations) |
classifier.ann.rdw | Float |
Initial value Delta_0 of update-values Delta_ij in RPROP method |
classifier.ann.rdwm | Float |
Update-values lower limit Delta_min in RPROP method |
classifier.ann.term | Choices |
Termination criteria |
classifier.ann.term iter | Choice |
Maximum number of iterations |
classifier.ann.term eps | Choice |
Epsilon |
classifier.ann.term all | Choice |
Max. iterations + Epsilon |
classifier.ann.eps | Float |
Epsilon value used in the Termination criteria |
classifier.ann.iter | Int |
Maximum number of iterations used in the Termination criteria |
classifier.rf.max | Int |
Maximum depth of the tree |
classifier.rf.min | Int |
Minimum number of samples in each node |
classifier.rf.ra | Float |
Termination Criteria for regression tree |
classifier.rf.cat | Int |
Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split |
classifier.rf.var | Int |
Size of the randomly selected subset of features at each tree node |
classifier.rf.nbtrees | Int |
Maximum number of trees in the forest |
classifier.rf.acc | Float |
Sufficient accuracy (OOB error) |
classifier.knn.k | Int |
Number of Neighbors |
rand | Int |
set user defined seed |
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inxml | XML input parameters file |
Load otb application from xml file |
outxml | XML output parameters file |
Save otb application to xml file |
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Input and output data This group of parameters allows setting input and output data.
Field names for training features. List of field names in the input vector data to be used as features for training.
Field containing the class id for supervision Field containing the class id for supervision. Only geometries with this field available will be taken into account.
Layer Index Index of the layer to use in the input vector file.
Validation data This group of parameters defines validation data.
Classifier to use for the training Choice of the classifier to use for the training. Available choices are:
set user defined seed Set specific seed. with integer value.
Load otb application from xml file Load otb application from xml file
Save otb application to xml file Save otb application to xml file
To run this example in command-line, use the following:
To run this example from Python, use the following code snippet:
This application has been written by OTB Team.