VectorClassifier - Vector Classification

Performs a classification of the input vector data according to a model file.Features of the vector data output will contain the class labels decided by the classifier (maximal class label = 65535).

Detailed description

This application performs a vector data classification based on a model file produced by the TrainVectorClassifier application.


This section describes in details the parameters available for this application. Table [1] presents a summary of these parameters and the parameters keys to be used in command-line and programming languages. Application key is VectorClassifier .

[1]Table: Parameters table for Vector Classification.
Parameter Key Parameter Name Parameter Type
in Name of the input vector data Input vector data
instat Statistics file Input File name
model Model file Input File name
cfield Field containing the predicted class String
feat Field names to be calculated. List
confmap Confidence map Boolean
out Output vector data file containing class labels Output File name
inxml Load otb application from xml file XML input parameters file
outxml Save otb application to xml file XML output parameters file
  • Name of the input vector data: The input vector data to classify.
  • Statistics file: A XML file containing mean and standard deviation to centerand reduce samples before classification (produced by ComputeImagesStatistics application).
  • Model file: A model file (produced by TrainVectorClassifier application,maximal class label = 65535).
  • Field containing the predicted class: Field containing the predicted class.Only geometries with this field available will be taken into account.
  • Field names to be calculated.: List of field names in the input vector data used as features for training.
  • Confidence map: Confidence map of the produced classification. The confidence index depends on the model : - LibSVM : difference between the two highest probabilities (needs a model with probability estimates, so that classes probabilities can be computed for each sample) - OpenCV * Boost : sum of votes * DecisionTree : (not supported) * GradientBoostedTree : (not supported) * KNearestNeighbors : number of neighbors with the same label * NeuralNetwork : difference between the two highest responses * NormalBayes : (not supported) * RandomForest : Confidence (proportion of votes for the majority class). Margin (normalized difference of the votes of the 2 majority classes) is not available for now. * SVM : distance to margin (only works for 2-class models) .
  • Output vector data file containing class labels: Output vector data file storing sample values (OGR format).If not given, the input vector data file is updated.
  • 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:

otbcli_VectorClassifier -in vectorData.shp -instat meanVar.xml -model svmModel.svm -out svmModel.svm -feat perimeter  area  width -cfield predicted

To run this example from Python, use the following code snippet:


# Import the otb applications package
import otbApplication

# The following line creates an instance of the VectorClassifier application
VectorClassifier = otbApplication.Registry.CreateApplication("VectorClassifier")

# The following lines set all the application parameters:
VectorClassifier.SetParameterString("in", "vectorData.shp")

VectorClassifier.SetParameterString("instat", "meanVar.xml")

VectorClassifier.SetParameterString("model", "svmModel.svm")

VectorClassifier.SetParameterString("out", "svmModel.svm")

# The following line execute the application


Shapefiles are supported. But the SQLite format is only supported in update mode.


This application has been written by OTB-Team.

See Also

These additional resources can be useful for further information: