Revision as of 17:28, 8 December 2018

Classification with Orfeo Toolbox

Training of per-pixel classifier

• Type into the search box of the Windows taskbar: mapla.bat. Click on mapla.bat to open Monteverdi Application Launcher.
• In the search engine of mapla, type TrainImages and double click TrainImagesClassifer.
• Specify a multispectral image as Input Image: the Sentinel-2 image Subset_S2A_MSIL2A_20170619T_MUL_BOA.tif
• Specify directory and name for the XML Output image. Specify the extension .xml for this file.
• (see figure B).
• Set Subset_S2A_MSIL2A_20170619T_MUL_BOA.tif as Input image list.
• Set lab07_training_input.shp as Input vector list.
• Set Subset_S2A_MSIL2A_20170619T_MUL_BOA.tif.xml as Input XML image statistics file.
• Set Name of discrimination field to C_ID (C_ID refers to the column that contains the LUC code).
• Save the Output confusion matrix as ConfusionMatrixSVM.csv.
• Save the Output model as SVM.model.
• Click on Execute.
• Calculation of accuracies :
  Open ConfusionMatrixSVM.csv in LibreOffice or MS Excel and calculate overall, producer and consumer accuracies.

Classification

1. Open Orfeo Toolbox --> Image Classification (see figure C).
2. Set Subset_S2A_MSIL2A_20170619T_MUL_BOA.tif as Input image.
3. Set SVM.model as Model file.
4. Set Subset_S2A_MSIL2A_20170619T_MUL_BOA.xml as Statistical file.
5. Save the Output image as su_svm.tif.
6. Evaluate classification results.
   1. Add the classification result su_svm.tif to QGIS.
   2. Right click su_svm.tif in the TOC and select Properties --> Style --> Style --> Load Style.
   3. Load lab05_MinDist.qml.

Compute a confusion matrix with independent reference data

1. Open Orfeo Toolbox --> ComputeConfusionMatrix (Vector).
2. Set su_svm.tif as Input image.
3. Set lab05_validation.shp as Input reference vector data.
4. Set Field name to C_ID.