Supervised classification (Tutorial)

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== Classification with Orfeo Toolbox ==
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=Per pixel classification with QGIS and OTB processing plugin=
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=== Image statistics ===
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In '''OTB Version 7.0.0''' the requirements for the input training vector data are very restrictive because of some bugs.
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# Add the LandSat-8 imagery ''Subset_S2A_MSIL2A_20170619T_B12_BOA.tif '' into a QGIS project.
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** File format needs to be Shapefile (GPKG does not work).
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# Calculate mean and standard error for each band of the Sentinel-2 imagery.
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** The column containing the class label needs to integer (whole number). '''Data type Integer 64bit does not work!'''
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## Open {{mitem|text=Orfeo Toolbox --> Compute images second order statistics}} (see figure '''A''').
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** 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.  
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## Set ''Subset_S2A_MSIL2A_20170619T_B12_BOA.tif'' as {{button|text=Input images}}.
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A work around is to make a copy of your training data and delete all other columns than the class label column
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## Save the {{button|text=Output XML file}} as ''Subset_S2A_MSIL2A_20170619T_B12_BOA.tif.xml''.
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=== Train image classifier ===
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All these issues have been fixed in '''OTB Version 7.1.0'''!
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# Add the training areas ''lab_9_training_input.shp'' into QGIS. It should be available in the course data.
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# Open {{mitem|text=Orfeo Toolbox --> TrainImageClassifier (libsvm)}} to use the Support Vector Machine SVM algorithm (see figure '''B''').
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# Set ''Subset_S2A_MSIL2A_20170619T_B12_BOA.tif'' as {{button|text=Input image list}}.
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# Set ''training_manual_poly.shp'' as {{button|text=Input vector list}}.
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# Set ''Subset_S2A_MSIL2A_20170619T_B12_BOA.tif.xml'' as {{button|text=Input XML image statistics file}}.
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# Set {{button|text=Name of discrimination field}} to ''C_ID'' (C_ID refers to the column that contain the LUC classes code).
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# Save the {{button|text=Output confusion matrix}} as ''ConfusionMatrixSVM.csv''.
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# Save the {{button|text=Output model}} as ''SVM.model''.
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# Calculation of accuracies :<br/> Open ''ConfusionMatrixSVM.csv'' in LibreOffice or MS Excel and calculate overall, producer and consumer accuracies.
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=== Classification===
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==Training phase==
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# Add the cloud mask ''cloud_shadow_mask.tif'' into QGIS. It should be available in the course data.
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* In the search engine of Processing Toolbox, type {{typed|text=TrainImages}} and open '''TrainImagesClassifer'''.
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# Open {{mitem|text=Orfeo Toolbox --> Image Classification}} (see figure '''C''').
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* In the {{button|text=Input Image List}} select a (or optional: several) multispectral images: '''mosaic_S2A_MSIL2A_20200521T102031_N0214_R065_T32UNC_UNB_UPC.tif'''.
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# Set ''Subset_S2A_MSIL2A_20170619T_B12_BOA.tif'' as {{button|text=Input image}}.
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* In the {{button|text=Input Vector Data List}} choose a vector polygon file with training areas: '''lucc_training_2020-05-21.shp'''.
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# Set ''cloud_shadow_mask.tif'' as {{button|text=Input mask}}.
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* In the {{button|text=Validation Vector Data List}} and choose a vector polygon file with an independent sample of validation areas: leave empty.
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# Set ''SVM.model'' as {{button|text=Model file}}.
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* 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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# Set ''SUB_LC81950242013188LGN00_MUL.tif.xml'' as {{button|text=Statistical file}}.
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* Type {{typed|text=Code}} in the {{button|text=Field Name}} text field.
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# Save the {{button|text=Output image}} as ''su_svm.tif''.
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* Choose Support Vector Machine Classifer {{button|text=libsvm}} from the drop down list.
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# Evaluate classification results.
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* SVM Model Type is {{button|text=csvc}}
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## Add the classification result ''su_svm.tif'' to QGIS.
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* 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!)
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## Right click ''su_svm.tif'' in the [[TOC]] and select {{mitem|text=Properties --> Style --> Style --> Load Style}}.
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* The SVM Kernel Type is {{button|text=Linear}}.
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## Load ''classifcation.qml'', which should be available in the course data.  
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* 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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## Open the Google Maps Layer under {{mitem|text=Web --> ObenLayers plugin --> Add Google Satellite layer}} and look for misclassification.
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* In the {{button|text=Output model}} specify an model file: e.g. '''svm.model'''
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* Define an output file for {{button|text=Output confusion matrix or contingency table}} (e.g.'''svm_matrix.csv''').
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* Click {{button|text=Run}}.
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* Click on the {{button|text=Log}} tab and inspect the '''model quality''' measures: Precision, Recall, F-score and Kappa index.
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[[File:qgis_otb_trainimages.png|400px]]
  
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=== Compute a confusion matrix with independent reference data ===
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==Classification phase==
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# Open {{mitem|text=Orfeo Toolbox --> ComputeConfusionMatrix (Vector)}} (see figure '''D''').
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* In the search engine of Processing Toolbox, type {{typed|text=ImageClassifier}} and double click '''ImageClassifier'''.
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# Set ''su_svm.tif'' as {{button|text=Input image}}.
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* Set ''mosaic_S2A_MSIL2A_20200521T102031_N0214_R065_T32UNC_UNB_UPC.tif'' as {{button|text=Input image}}.
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# Set ''train_systematic_seg.shp'' as {{button|text=Input reference vector data}}.
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* Set {{button|text=Input _mask}} to blank (top of drop-down list).
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# Set {{button|text=Field name}} to ''C_ID''.
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* Set '''lucc_svm.model''' as {{button|text=Model file}}.
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* Set the number of classes in your model: this is the number of unique classes in your training file.
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* Set {{button|text=Output pixel type}} to '''uint16'''
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* Save the {{button|text=Output image}} as '''svm_classification.tif'''.
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* Uncheck Confidence map: ''Open output file after running algorithm''.
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* Uncheck Probability map: ''Open output file after running algorithm''.
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* Add ''svm_classification.tif'' to QGIS canvas.
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* Find the prepared style file '''rast_classifcation.qml''' in the Tutorial data of Workshop 05.
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* Right click ''svm_classification.tif'' in the [[TOC]] and select {{mitem|text=Properties --> Symbology --> Style --> Load Style}}.
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* Select the style file '''svm_classification.tif'''. {{button|text=OK}}.
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[[File:qgis_otb_imageclassifier.png|400px]]
  
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==Postprocessing==
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This application uses a majority filter with a circular structure element.
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* In the search engine of Processing Toolbox, type {{typed|text=regularization}} and double click '''ClassificationMapRegularization'''.
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* Define {{button|text=Input classification image}} as an classification output raster.
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* Set {{button|text=Structuring element radius}} to {{typed|text=2}} pixels.
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* Set {{button|text=Output pixel type}} to {{button|text=uint16}}.
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* Save the {{button|text=Output regularizd image}} as '''svm_classification_majority.tif'''.
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* Click {{button|text=Run}}.
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[[File:qgis_otb_majority.png|400px]]
  
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=Per pixel classification with OTB standalone=
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==Training phase==
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* Type into the search box of the Windows taskbar: {{typed|text=mapla.bat}}. Click on mapla.bat to open  Monteverdi Application Launcher.
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* In the search engine of mapla, type {{typed|text=TrainImages}} and double click '''TrainImagesClassifer'''.
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* 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 '''.
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* In the {{button|text=Input Vector Data List}} choose a vector polygon file with training areas: '''lucc_training_input.shp'''.
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* 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'''
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* In the {{button|text=Output model}} specify an output model file: e.g. '''lucc_svm.model'''
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* Activate the checkbox and save the {{button|text=Output confusion matrix or contingency table}} as '''lucc_svm_confusion.csv'''.
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* In the {{button|text=Bound sample number by minimum}} field type {{typed|text=1}}.
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* Set the {{button|text=training and validation sample ratio}} to {{typed|text=0 }}. (0 = all training data).
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* 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).
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* Choose {{button|text=LibSVM classifier}} from the drop down list as Classifier to use for the training.
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* The SVM Kernel Type is {{button|text=Linear}}.
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* The SVM Model Type is {{button|text=C support vector classification}}.
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* Switch the Parameters optimization to {{button|text=on}}.
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* Check user defined seed and enter an integer value.
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* Click on {{button|text=Execute}}.
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* Click on the {{button|text=Log}} tab and inspect the quality measures: Precision, Recall, F-score and Kappa index.
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[[File:otb_trainimages.png|500px]]
  
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==Classification phase==
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* In the search engine of mapla, type {{typed|text=ImageClassifier}} and double click '''ClassifcationMapRegularization'''
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* Set ''Subset_S2A_MSIL2A_20170619T_MUL.tif'' as {{button|text=Input image}}.
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* Set '''SVM.model''' as {{button|text=Model file}}.
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* Save the {{button|text=Output image}} as '''svm_classification.tif'''.
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[[File:otb_imageclassifier.png|500px]]
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* Evaluate classification results:
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* Add ''svm_classification.tif'' to QGIS canvas.
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* Download the style file '''classifcation.qml''' from Stud.IP.
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* Right click ''svm_classification.tif'' in the [[TOC]] and select {{mitem|text=Properties --> Style --> Style --> Load Style}}.
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* Select the style file '''\lucc\classification.qml'''. {{button|text=OK}}.
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* Open the text file '''lucc_svm_confusion.csv''' with LibreOffice Calc or MS Excel and calculate overall, user, producer accuracy and kappa index.
  
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=== Figures ===
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==Postprocessing==
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{|class="wikitable" style="border:0pt"
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This application uses a majority with a circular structure element.
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|style="border:0pt"|[[Image:ComputeImage.png|thumb|450px|'''Figure A''': Dialogues of the ''Compute images second order statistics'']]
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* In the search engine of mapla, type, {{typed|text=regular}} and double click '''ClassifcationMapRegularization'''.
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|style="border:0pt"|[[Image:TrainImageClassifier.png|thumb|450px|'''Figure B''': Dialogues of the ''TrainImageClassifier (libsvm)'']]
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* Define {{button|text=Input classification image}} as ans classification output raster.
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|-
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* Save the {{button|text=Output regularizd image}} as '''svm_classification_majority.tif'''.
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|style="border:0pt"|[[Image:ImageClassification.png|thumb|450px|'''Figure C''': Dialogues of the ''Image Classification'']]
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* Set {{button|text=Structuring element radius}} to {{typed|text=3}} pixels.
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|style="border:0pt"|[[Image:ComputeConfusionMatrix.png|thumb|450px|'''Figure D''': Dialogues of the ''ComputeConfusionMatrix (Vector)'']]
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* Set {{button|text=Output pixel type}} to {{button|text=uint8}}.
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|}
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* Click {{button|text=Execute}}.
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[[File:otb_majority.png|400px]]
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[[Category:Image classification]]
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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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