# OGRLayerClassifier

## Brief Description

Classify an OGR layer based on a machine learning model and a list of features to consider.

## Tags

Segmentation

## Long Description

This application will apply a trained machine learning model on the selected feature to get a classification of each geometry contained in an OGR layer. The list of feature must match the list used for training. The predicted label is written in the user defined field for each geometry.

## Parameters

- [param] -inshp <string> Name of the input shapefile. Mandatory: True. Default Value: ""
- [param] -instats <string> XML file containing mean and variance of each feature.. Mandatory: True. Default Value: ""
- [param] -insvm <string> Input model filename.. Mandatory: True. Default Value: ""
- [param] -cfield <string> Field containing the predicted class. Mandatory: True. Default Value: "predicted"
- [param] -inxml <string> Load otb application from xml file. Mandatory: False. Default Value: ""
- [param] -outxml <string> Save otb application to xml file. Mandatory: False. Default Value: ""
[choice] -feat Features to be calculated . Mandatory: True. Default Value: ""

## Limitations

Experimental. Only shapefiles are supported for now.

## Authors

David Youssefi during internship at CNES

## See Also

ComputeOGRLayersFeaturesStatistics,TrainOGRLayersClassifier

## Example of use

- inshp: vectorData.shp

- instats: meanVar.xml

- insvm: svmModel.svm

- feat: perimeter

- cfield: predicted