Per pixel supervised classification

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* In the {{button|text=Input Image List}} select one (or optional: several) multi-band raster (multispectral images).
 
* In the {{button|text=Input Image List}} select one (or optional: several) multi-band raster (multispectral images).
 
* In the {{button|text=Input Vector Data List}} '''do not''' select a vector file from the list of files which might be already loaded in the QGIS Viewer. There is currently a bug in QGIS 3.16 which leads to failure during file import. Instead please select a vector file clicking [[File:Qgis_add_file.png]] and browse directly to the file containing training area polygons in format GPKG or SHP.
 
* In the {{button|text=Input Vector Data List}} '''do not''' select a vector file from the list of files which might be already loaded in the QGIS Viewer. There is currently a bug in QGIS 3.16 which leads to failure during file import. Instead please select a vector file clicking [[File:Qgis_add_file.png]] and browse directly to the file containing training area polygons in format GPKG or SHP.
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* In the {{button|text=Validation Vector Data List}} If you do not have independent validation data: leave empty.
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* If you do not have independent validation data: leave {{button|text=Validation Vector Data List}} empty.
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* Bound sample number to minimum: {{typed|text=1}}. The class with the minimum number of pixels determines the sample  size of all other classes. Changing this value to {{typed|text=0}} does not have an effect (current bug in QGIS3.16).  
+
* Bound sample number ba minimum: {{typed|text=1}}. The class with the minimum number of pixels determines the sample  size of all other classes. Changing this value to {{typed|text=0}} does not have an effect (current bug in QGIS3.16).  
 
* Training and validation sample ratio: {{typed|text=0.25}}. This is a 4-fold cross-validation with split 0.75 per cent  for training and 0.25 per cent for testing.
 
* Training and validation sample ratio: {{typed|text=0.25}}. This is a 4-fold cross-validation with split 0.75 per cent  for training and 0.25 per cent for testing.
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* Type {{typed|text=C_ID}} in the {{button|text=Field Name}} text field.
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* Type {{typed|text=C_ID}} in the {{button|text=Field containing the class integer label for supervision}} text field.
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* Choose Support Vector Machine Classifer {{button|text=libsvm}} from the drop down list.
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* As Classifier to use for training choose Support Vector Machine {{button|text=libsvm}} from the drop down list.
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* SVM Model Type is {{button|text=csvc}}
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* The SVM Kernel Type is {{button|text=Linear}}.
 
* The SVM Kernel Type is {{button|text=Linear}}.
 +
* SVM Model Type is {{button|text=csvc}}
 
* Switch checkbox {{button|text=Parameters optimization}} '''off'''. The optimization results in a higher accuracy but takes much time (> 1 hour computation).
 
* Switch checkbox {{button|text=Parameters optimization}} '''off'''. The optimization results in a higher accuracy but takes much time (> 1 hour computation).
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* In the {{button|text=Output model}} specify an model file: e.g. '''svm.model'''
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* In the {{button|text=Output model}} specify a model file: e.g. '''svm.model'''
 
* Click {{button|text=Run}}.
 
* Click {{button|text=Run}}.
 
* Click on the {{button|text=Log}} tab and inspect the '''model quality''' measures: Precision, Recall, F-score and Kappa index.
 
* Click on the {{button|text=Log}} tab and inspect the '''model quality''' measures: Precision, Recall, F-score and Kappa index.

Revision as of 23:03, 12 December 2020

Contents

Using QGIS and OTB processing toolbox

  • Using OTB version 7.2.0, input vector file format of training data can be GeoPackage GPKG or ESRI Shapefile.
  • The column containing class label needs to be an integer (whole number).

Training phase

  • In the search engine of Processing Toolbox, type TrainImages and open TrainImagesClassifer.
  • In the Input Image List select one (or optional: several) multi-band raster (multispectral images).
  • In the Input Vector Data List do not select a vector file from the list of files which might be already loaded in the QGIS Viewer. There is currently a bug in QGIS 3.16 which leads to failure during file import. Instead please select a vector file clicking Qgis add file.png and browse directly to the file containing training area polygons in format GPKG or SHP.
  • If you do not have independent validation data: leave Validation Vector Data List empty.
  • Bound sample number ba minimum: 1. The class with the minimum number of pixels determines the sample size of all other classes. Changing this value to 0 does not have an effect (current bug in QGIS3.16).
  • Training and validation sample ratio: 0.25. This is a 4-fold cross-validation with split 0.75 per cent for training and 0.25 per cent for testing.
  • Type C_ID in the Field containing the class integer label for supervision text field.
  • As Classifier to use for training choose Support Vector Machine libsvm from the drop down list.
  • The SVM Kernel Type is Linear.
  • SVM Model Type is csvc
  • Switch checkbox Parameters optimization off. The optimization results in a higher accuracy but takes much time (> 1 hour computation).
  • In the Output model specify a model file: e.g. svm.model
  • Click Run.
  • Click on the Log tab and inspect the model quality measures: Precision, Recall, F-score and Kappa index.

Qgis otb trainimages.png

Classification phase

  • In the search engine of Processing Toolbox, type ImageClassifier and double click ImageClassifier.
  • Set an multiband image as Input image.
  • Set Input _mask to blank (top of drop-down list).
  • Set svm.model as Model file.
  • Set the number of classes in your model: this is the number of unique classes in your training file.
  • Set Output pixel type to uint8
  • Save the Output image as svm_classification.tif.
  • Uncheck Confidence map: Open output file after running algorithm.
  • Uncheck Probability map: Open output file after running algorithm.
  • Add svm_classification.tif to QGIS canvas.
  • Find the prepared style file classified.qml.
  • Right click svm_classification.tif in the TOC and select Properties --> Symbology --> Style --> Load Style.
  • Select the style file classified.qml. OK.

Qgis otb imageclassifier.png

Postprocessing

This application uses a majority filter with a circular structure element.

  • In the search engine of Processing Toolbox, type regularization and double click ClassificationMapRegularization.
  • Define Input classification image as an classification output raster.
  • Set Structuring element radius to 2 pixels.
  • Set Output pixel type to uint8.
  • Save the Output regularizd image as svm_classification_majority.tif.
  • Click Run.

Qgis otb majority.png

Using 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 in file format GPKG or SHP.
  • In the Output model specify an output model file: e.g. lucc_svm.model
  • Activate the checkbox and save the Output confusion matrix or contingency table as lucc_svm_confusion.csv.
  • In the Bound sample number by minimum field type 0.
  • Set the training and validation sample ratio to 0. (0 = all training data).
  • Mark C_ID in the Field containing the class integer label (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.
  • The SVM Model Type is C support vector classification.
  • Switch the Parameters optimization to on.
  • Check user defined seed and enter an integer value.
  • Click on Execute.
  • Click on the Log tab and inspect the quality measures: Precision, Recall, F-score and Kappa index.

Otb trainimages.png

Classification phase

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

Otb imageclassifier.png

  • Evaluate classification results:
  • Add svm_classification.tif to QGIS canvas.
  • Download the style file classified.qml from Stud.IP.
  • Right click svm_classification.tif in the TOC and select Properties --> Style --> Style --> Load Style.
  • Select the style file classified.qml. OK.

Postprocessing

This application uses a majority with a circular structure element.

  • In the search engine of mapla, type, regular and double click ClassifcationMapRegularization.
  • Define Input classification image as ans classification output raster.
  • Save the Output regularizd image as svm_classification_majority.tif.
  • Set Structuring element radius to 2 pixels.
  • Set Output pixel type to uint8.
  • Click Execute.

Otb majority.png

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