Revision as of 14:00, 10 December 2020

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 aone (or optional: several) multiband 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 and browse directly to the file containing training area polygons in format GPKG or SHP.
• In the Validation Vector Data List and choose a vector polygon file with an independent sample of validation areas: leave empty.
• Bound sample number to minimum: 1. The class with the minimum number of pixels dtermines the sample size of all other classes. Chnaging 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 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.

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.

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.

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: 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.

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.

• 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.

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.