Supervised classification (Tutorial)

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(Image statistics)
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== Train image classifier ==
 
== Train image classifier ==
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* In the search engine of mapla, type {{typed|text=TrainImages} and double click '''TrainImagesClassifer'''.
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* Specify a multispectral image as Input Image: the Sentinel-2 image ''Subset_S2A_MSIL2A_20170619T_MUL_BOA.tif ''
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* Specify directory and name for the XML Output image. Specify the extension '''.xml''' for this file.
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* Click on {{button|text=Execute}}.
 
# Add the training areas as vector polygon file ''lab05_training_input.shp'' into QGIS.  
 
# Add the training areas as vector polygon file ''lab05_training_input.shp'' into QGIS.  
 
# Open {{mitem|text=Orfeo Toolbox --> TrainImageClassifier (libsvm)}} to use the Support Vector Machine SVM algorithm (see figure '''B''').
 
# Open {{mitem|text=Orfeo Toolbox --> TrainImageClassifier (libsvm)}} to use the Support Vector Machine SVM algorithm (see figure '''B''').

Revision as of 15:04, 2 December 2018

Contents

Classification with Orfeo Toolbox

Image statistics

  • 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 ComputeImagesStatistics and double click ComputeImagesStatistics.
  • 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.
  • Click on Execute.

Qgis ComputeImagesStatistics.png

Train image classifier

  • In the search engine of mapla, type {{typed|text=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.
  • Click on Execute.
  1. Add the training areas as vector polygon file lab05_training_input.shp into QGIS.
  2. Open Orfeo Toolbox --> TrainImageClassifier (libsvm) to use the Support Vector Machine SVM algorithm (see figure B).
  3. Set Subset_S2A_MSIL2A_20170619T_MUL_BOA.tif as Input image list.
  4. Set lab05_training_input.shp as Input vector list.
  5. Set Subset_S2A_MSIL2A_20170619T_MUL_BOA.tif.xml as Input XML image statistics file.
  6. Set Name of discrimination field to C_ID (C_ID refers to the column that contains the LUC code).
  7. Save the Output confusion matrix as ConfusionMatrixSVM.csv.
  8. Save the Output model as SVM.model.
  9. 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.
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