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=
 +
In '''OTB Version 7.0.0''' the requirements for the input training vector data are very restrictive because of some bugs.
 +
** File format needs to be Shapefile (GPKG does not work).
 +
** The column containing the class label needs to integer (whole number). '''Data type Integer 64bit does not work!'''
 +
** Any other columns with other data types (real or text (string) with umlauts or blank are not correctly imported which leads to failure of the modul TrainImagesClassifier.
 +
A work around is to make a copy of your training data and delete all other columns than the class label column
 +
 +
All these issues have been fixed in '''OTB Version 7.1.0'''!
 +
 
==Training phase==
 
==Training phase==
 +
* In the search engine of Processing Toolbox, type {{typed|text=TrainImages}} and open '''TrainImagesClassifer'''.
 +
* In the {{button|text=Input Image List}} select a (or optional: several) multispectral images: '''mosaic_S2A_MSIL2A_20200521T102031_N0214_R065_T32UNC_UNB_UPC.tif'''.
 +
* In the {{button|text=Input Vector Data List}} choose a vector polygon file with training areas: '''lucc_training_2020-05-21.shp'''.
 +
* In the {{button|text=Validation Vector Data List}} and choose a vector polygon file with an independent sample of validation areas: leave empty.
 +
* 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.
 +
* Type {{typed|text=Code}} in the {{button|text=Field Name}} text field.
 +
* Choose Support Vector Machine Classifer {{button|text=libsvm}} from the drop down list.
 +
* SVM Model Type is {{button|text=csvc}}
 +
* User defined input centroids: Type any character, no need for a filename. This field is required and but ignored (a bug in the OTB plugin!)
 +
* The SVM Kernel Type is {{button|text=Linear}}.
 +
* Switch checkbox {{button|text=Parameters optimization}} '''off'''. The optimization results in a higher accuracy but takes much time (> 1 hour computation).
 +
* In the {{button|text=Output model}} specify an model file: e.g. '''svm.model'''
 +
* Define an output file for {{button|text=Output confusion matrix or contingency table}} (e.g.'''svm_matrix.csv''').
 +
* Click {{button|text=Run}}.
 +
* Click on the {{button|text=Log}} tab and inspect the '''model quality''' measures: Precision, Recall, F-score and Kappa index.
 +
[[File:qgis_otb_trainimages.png|400px]]
 +
 
==Classification phase==
 
==Classification phase==
 +
* In the search engine of Processing Toolbox, type {{typed|text=ImageClassifier}} and double click '''ImageClassifier'''.
 +
* Set ''mosaic_S2A_MSIL2A_20200521T102031_N0214_R065_T32UNC_UNB_UPC.tif'' as {{button|text=Input image}}.
 +
* Set {{button|text=Input _mask}} to blank (top of drop-down list).
 +
* Set '''lucc_svm.model''' as {{button|text=Model file}}.
 +
* Set the number of classes in your model: this is the number of unique classes in your training file.
 +
* Set {{button|text=Output pixel type}} to '''uint16'''
 +
* Save the {{button|text=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 '''rast_classifcation.qml''' in the Tutorial data of Workshop 05.
 +
* Right click ''svm_classification.tif'' in the [[TOC]] and select {{mitem|text=Properties --> Symbology --> Style --> Load Style}}.
 +
* Select the style file '''svm_classification.tif'''. {{button|text=OK}}.
 +
[[File:qgis_otb_imageclassifier.png|400px]]
 +
 +
==Postprocessing==
 +
This application uses a majority filter with a circular structure element.
 +
* In the search engine of Processing Toolbox, type {{typed|text=regularization}} and double click '''ClassificationMapRegularization'''.
 +
* Define {{button|text=Input classification image}} as an classification output raster.
 +
* Set {{button|text=Structuring element radius}} to {{typed|text=2}} pixels.
 +
* Set {{button|text=Output pixel type}} to {{button|text=uint16}}.
 +
* Save the {{button|text=Output regularizd image}} as '''svm_classification_majority.tif'''.
 +
* Click {{button|text=Run}}.
 +
[[File:qgis_otb_majority.png|400px]]
  
 
=Per pixel classification with OTB standalone=
 
=Per pixel classification with OTB standalone=
Line 8: Line 57:
 
* In the search engine of mapla, type {{typed|text=TrainImages}} and double click '''TrainImagesClassifer'''.
 
* In the search engine of mapla, type {{typed|text=TrainImages}} and double click '''TrainImagesClassifer'''.
 
* In the {{button|text=Input Image List}} click on {{button|text=+}} and select a (or optional: several) multispectral images: '''Subset_S2A_MSIL2A_20170619T_MUL.tif '''.
 
* In the {{button|text=Input Image List}} click on {{button|text=+}} and select a (or optional: several) multispectral images: '''Subset_S2A_MSIL2A_20170619T_MUL.tif '''.
* In the {{button|text=Input Vector Data List}} choose a vector polygon file with training areas: '''lab07_training_input.shp'''.
+
* In the {{button|text=Input Vector Data List}} choose a vector polygon file with training areas: '''lucc_training_input.shp'''.
* Activate the checkbox {{button|text=Validation Vector Data List}} and choose a vector polygon file with an independent sample of validation areas: '''lab07_validation_input.shp'''
+
* Activate the checkbox {{button|text=Validation Vector Data List}} and choose a vector polygon file with an independent sample of validation areas: '''lucc_validation.shp'''
* In the {{button|text=Output model}} specify an output model file: e.g. '''SVM.model'''
+
* In the {{button|text=Output model}} specify an output model file: e.g. '''lucc_svm.model'''
* Activate the checkbox and save the {{button|text=Output confusion matrix or contingency table}} as '''ConfusionMatrixSVM.csv'''.
+
* Activate the checkbox and save the {{button|text=Output confusion matrix or contingency table}} as '''lucc_svm_confusion.csv'''.
 
* In the {{button|text=Bound sample number by minimum}} field type {{typed|text=1}}.
 
* In the {{button|text=Bound sample number by minimum}} field type {{typed|text=1}}.
 
* Set the {{button|text=training and validation sample ratio}} to {{typed|text=0 }}. (0 = all training data).
 
* Set the {{button|text=training and validation sample ratio}} to {{typed|text=0 }}. (0 = all training data).
* Set {{button|text=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).
+
* Mark  ''C_ID''  in the {{button|text=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 {{button|text=LibSVM classifier}} from the drop down list as Classifier to use for the training.
 
* Choose {{button|text=LibSVM classifier}} from the drop down list as Classifier to use for the training.
 
* The SVM Kernel Type is {{button|text=Linear}}.
 
* The SVM Kernel Type is {{button|text=Linear}}.
 +
* The SVM Model Type is {{button|text=C support vector classification}}.
 
* Switch the Parameters optimization to {{button|text=on}}.
 
* Switch the Parameters optimization to {{button|text=on}}.
* Set user defined seed with an integer value.
+
* Check user defined seed and enter an integer value.
 
* Click on {{button|text=Execute}}.
 
* Click on {{button|text=Execute}}.
[[File:qgis-otb-trainImagesClassifier_svm.png|500px]]
+
* Click on the {{button|text=Log}} tab and inspect the quality measures: Precision, Recall, F-score and Kappa index.
 +
[[File:otb_trainimages.png|500px]]
  
 
==Classification phase==
 
==Classification phase==
* In the search engine of mapla, type {{typed|text=ImageClassifier}} and double click '''ImageClassifier'''
+
* In the search engine of mapla, type {{typed|text=ImageClassifier}} and double click '''ClassifcationMapRegularization'''
 
* Set ''Subset_S2A_MSIL2A_20170619T_MUL.tif'' as {{button|text=Input image}}.
 
* Set ''Subset_S2A_MSIL2A_20170619T_MUL.tif'' as {{button|text=Input image}}.
 
* Set '''SVM.model''' as {{button|text=Model file}}.
 
* Set '''SVM.model''' as {{button|text=Model file}}.
 
* Save the {{button|text=Output image}} as '''svm_classification.tif'''.
 
* Save the {{button|text=Output image}} as '''svm_classification.tif'''.
[[File:qgis-otb-ImageClassifier_SVM.png|500px]]
+
[[File:otb_imageclassifier.png|500px]]
 
* Evaluate classification results:
 
* Evaluate classification results:
** Load the multispectral Sentinel-2 image '''Subset_S2A_MSIL2A_20170619T_MUL.tif''' into QGIS.
+
* Add ''svm_classification.tif'' to QGIS canvas.
** {{mitem|text=Data source Manager --> Browser --> XYZ Tiles}}. Select Google Satellite as background layer.
+
* Download the style file '''classifcation.qml''' from Stud.IP.
** Load the European Urban Atlas as vector layer '''Subset-Goe_DE021L1_GOTTINGEN_UA2012_UTM32N.shp'''
+
* Right click ''svm_classification.tif'' in the [[TOC]] and select {{mitem|text=Properties --> Style --> Style --> Load Style}}.
** Add ''svm_classification.tif'' to the QGIS project.
+
* Select the style file '''\lucc\classification.qml'''. {{button|text=OK}}.
** Download the style file '''classifcation.qml''' from Stud.IP.
+
* Open the text file '''lucc_svm_confusion.csv''' with LibreOffice Calc or MS Excel and calculate overall, user, producer accuracy and kappa index.  
** Right click ''svm_classification.tif'' in the [[TOC]] and select {{mitem|text=Properties --> Style --> Style --> Load Style}}.
+
** Select the style file '''classification.qml'''. {{button|text=OK}}.
+
** Open the text file '''ConfusionMatrixSVM.csv''' and calculate overall, user and producer accuracies.  
+
  
[[Category:QGIS Tutorial]]
+
==Postprocessing==
 +
This application uses a majority with a circular structure element.
 +
* In the search engine of mapla, type, {{typed|text=regular}} and double click '''ClassifcationMapRegularization'''.
 +
* Define {{button|text=Input classification image}} as ans classification output raster.
 +
* Save the {{button|text=Output regularizd image}} as '''svm_classification_majority.tif'''.
 +
* Set {{button|text=Structuring element radius}} to {{typed|text=3}} pixels.
 +
* Set {{button|text=Output pixel type}} to {{button|text=uint8}}.
 +
* Click {{button|text=Execute}}.
 +
[[File:otb_majority.png|400px]]

Latest revision as of 14:12, 1 December 2020

Contents

[edit] Per pixel classification with QGIS and OTB processing plugin

In OTB Version 7.0.0 the requirements for the input training vector data are very restrictive because of some bugs.

    • File format needs to be Shapefile (GPKG does not work).
    • The column containing the class label needs to integer (whole number). Data type Integer 64bit does not work!
    • Any other columns with other data types (real or text (string) with umlauts or blank are not correctly imported which leads to failure of the modul TrainImagesClassifier.

A work around is to make a copy of your training data and delete all other columns than the class label column

All these issues have been fixed in OTB Version 7.1.0!

[edit] Training phase

  • In the search engine of Processing Toolbox, type TrainImages and open TrainImagesClassifer.
  • In the Input Image List select a (or optional: several) multispectral images: mosaic_S2A_MSIL2A_20200521T102031_N0214_R065_T32UNC_UNB_UPC.tif.
  • In the Input Vector Data List choose a vector polygon file with training areas: lucc_training_2020-05-21.shp.
  • In the Validation Vector Data List and choose a vector polygon file with an independent sample of validation areas: leave empty.
  • 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 Code in the Field Name text field.
  • Choose Support Vector Machine Classifer libsvm from the drop down list.
  • SVM Model Type is csvc
  • User defined input centroids: Type any character, no need for a filename. This field is required and but ignored (a bug in the OTB plugin!)
  • The SVM Kernel Type is Linear.
  • Switch checkbox Parameters optimization off. The optimization results in a higher accuracy but takes much time (> 1 hour computation).
  • In the Output model specify an model file: e.g. svm.model
  • Define an output file for Output confusion matrix or contingency table (e.g.svm_matrix.csv).
  • Click Run.
  • Click on the Log tab and inspect the model quality measures: Precision, Recall, F-score and Kappa index.

Qgis otb trainimages.png

[edit] Classification phase

  • In the search engine of Processing Toolbox, type ImageClassifier and double click ImageClassifier.
  • Set mosaic_S2A_MSIL2A_20200521T102031_N0214_R065_T32UNC_UNB_UPC.tif as Input image.
  • Set Input _mask to blank (top of drop-down list).
  • Set lucc_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 uint16
  • 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 rast_classifcation.qml in the Tutorial data of Workshop 05.
  • Right click svm_classification.tif in the TOC and select Properties --> Symbology --> Style --> Load Style.
  • Select the style file svm_classification.tif. OK.

Qgis otb imageclassifier.png

[edit] 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 uint16.
  • Save the Output regularizd image as svm_classification_majority.tif.
  • Click Run.

Qgis otb majority.png

[edit] Per pixel classification with OTB standalone

[edit] 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: lucc_training_input.shp.
  • Activate the checkbox Validation Vector Data List and choose a vector polygon file with an independent sample of validation areas: lucc_validation.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 1.
  • 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

[edit] 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 classifcation.qml from Stud.IP.
  • Right click svm_classification.tif in the TOC and select Properties --> Style --> Style --> Load Style.
  • Select the style file \lucc\classification.qml. OK.
  • Open the text file lucc_svm_confusion.csv with LibreOffice Calc or MS Excel and calculate overall, user, producer accuracy and kappa index.

[edit] 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 3 pixels.
  • Set Output pixel type to uint8.
  • Click Execute.

Otb majority.png

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