Revision as of 13:44, 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.
• In the Input Image List select a (or optional: several) multispectral images: Subset_S2A_MSIL2A_20170619T_MUL.tif .
• In the Validation Vector Data List and choose a vector polygon file with an independent sample of validation areas: lucc_validation.shp.
• Type C_ID in the {button|text=Field Name}} text field.
• Choose Support Vector Machine Classifer libsvm from the drop down list.
• SVM Model Type is csvc
• The SVM Kernel Type is Linear.
• Switch checkbox Parameters optimization on.
• In the Output model specify an model file: e.g. lucc_svm.model
• Define an output file for Output confusion matrix or contingency table (e.g.lucc_svm_confusion.csv).
• Click Run.
• Click on the Log tab and inspect the 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 Subset_S2A_MSIL2A_20170619T_MUL.tif as Input image.
• Set Input _mask to blank (top of drop-down list).
• Set lucc_svm.model as Model file.
• Set Output pixel type to uint8
• Save the Output image as svm_classification.tif.
• Uncheck Confidence map: Open output file after running algorithm.

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: 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.

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.

• 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.