Supervised classification (Tutorial)

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=Per pixel classification with QGIS and OTB processing plugin=
=Per pixel classification with QGIS and OTB processing plugin=
==Training phase==
==Training phase==
* In the search engine of Processing Toolbox, type {{typed|text=TrainImages}} and open '''TrainImagesClassifer'''.
==Classification phase==
==Classification phase==

Revision as of 09:02, 4 July 2019


Per pixel classification with QGIS and OTB processing plugin

Training phase

  • In the search engine of Processing Toolbox, type TrainImages and open TrainImagesClassifer.

Classification phase

Per pixel classification with OTB standalone

Training phase

  • 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.
  • In the Input Image List click on + and select a (or optional: several) multispectral images: Subset_S2A_MSIL2A_20170619T_MUL.tif .
  • In the Input Vector Data List choose a vector polygon file with training areas: lab07_training_input.shp.
  • Activate the checkbox Validation Vector Data List and choose a vector polygon file with an independent sample of validation areas: lab07_validation_input.shp
  • In the Output model specify an output model file: e.g. SVM.model
  • Activate the checkbox and save the Output confusion matrix or contingency table as ConfusionMatrixSVM.csv.
  • In the Bound sample number by minimum field type 1.
  • Set the training and validation sample ratio to 0. (0 = all training data).
  • Set Field containing the class integer label to C_ID (C_ID refers to the column that contains the LUC code in the training and validation vector file).
  • Choose LibSVM classifier from the drop down list as Classifier to use for the training.
  • The SVM Kernel Type is Linear.
  • Switch the Parameters optimization to on.
  • Set user defined seed with an integer value.
  • Click on Execute.

Qgis-otb-trainImagesClassifier svm.png

Classification phase

  • In the search engine of mapla, type ImageClassifier and double click ImageClassifier
  • Set Subset_S2A_MSIL2A_20170619T_MUL.tif as Input image.
  • Set SVM.model as Model file.
  • Save the Output image as svm_classification.tif.

Qgis-otb-ImageClassifier SVM.png

  • Evaluate classification results:
    • Load the multispectral Sentinel-2 image Subset_S2A_MSIL2A_20170619T_MUL.tif into QGIS.
    • Data source Manager --> Browser --> XYZ Tiles. Select Google Satellite as background layer.
    • Load the European Urban Atlas as vector layer Subset-Goe_DE021L1_GOTTINGEN_UA2012_UTM32N.shp
    • Add svm_classification.tif to the QGIS project.
    • Download the style file classifcation.qml from Stud.IP.
    • Right click svm_classification.tif in the TOC and select Properties --> Style --> Style --> Load Style.
    • Select the style file classification.qml. OK.
    • Open the text file ConfusionMatrixSVM.csv and calculate overall, user and producer accuracies.
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