Supervised classification (Tutorial)
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** Download the style file '''classifcation.qml''' from Stud.IP. | ** Download the style file '''classifcation.qml''' from Stud.IP. | ||
** Right click ''svm_classification.tif'' in the [[TOC]] and select {{mitem|text=Properties --> Style --> Style --> Load Style}}. | ** 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}} | + | ** Select the style file '''classification.qml'''. {{button|text=OK}}. |
| + | ** Open the text file '''SVMErrorMatrix.csv''' and report overall, user and producer accuracies. | ||
[[Category:QGIS Tutorial]] | [[Category:QGIS Tutorial]] | ||
Revision as of 18:44, 8 December 2018
Classification with Orfeo Toolbox
Training per-pixel classifiers
- 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: lab07_training_input.shp.
- Activate the checkbox Validation Vector Data List and choose a vector polygon file with an independent sample of validation areas: lab07_validation_input.shp
- In the Output model specify an output model file: e.g. SVM.model
- Activate the checkbox and save the Output confusion matrix or contingency table as ConfusionMatrixSVM.csv.
- In the Bound sample number by minimum field type 1.
- Set the training and validation sample ratio to 0. (0 = all training data).
- Set 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).
- Choose LibSVM classifier from the drop down list as Classifier to use for the training.
- The SVM Kernel Type is Gaussian radial basis function.
- Switch the Parameters optimization to on.
- Set user defined seed with an integer value.
- Click on Execute.
Classification
- 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 SVMErrorMatrix.csv and report overall, user and producer accuracies.
