Per pixel supervised classification
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* 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 | + | * In the {{button|text=Input Vector Data List}} choose a vector polygon file with training areas in file format GPKG or SHP. |
| − | + | ||
* In the {{button|text=Output model}} specify an output model file: e.g. '''lucc_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 '''lucc_svm_confusion.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= | + | * In the {{button|text=Bound sample number by minimum}} field type {{typed|text=0}}. |
* 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). | ||
* 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). | * 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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* Click on the {{button|text=Log}} tab and inspect the quality measures: Precision, Recall, F-score and Kappa index. | * Click on the {{button|text=Log}} tab and inspect the quality measures: Precision, Recall, F-score and Kappa index. | ||
[[File:otb_trainimages.png|500px]] | [[File:otb_trainimages.png|500px]] | ||
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==Classification phase== | ==Classification phase== | ||
* In the search engine of mapla, type {{typed|text=ImageClassifier}} and double click '''ClassifcationMapRegularization''' | * In the search engine of mapla, type {{typed|text=ImageClassifier}} and double click '''ClassifcationMapRegularization''' | ||
Revision as of 14:08, 10 December 2020
Contents |
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 uint8.
- 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 in file format GPKG or 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 0.
- 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.
