Supervised classification (Tutorial)
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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. | * Type into the search box of the Windows taskbar: {{typed|text=mapla.bat}}. Click on mapla.bat to open Monteverdi Application Launcher. | ||
* 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 | + | * 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: '''lab07_training_input.shp'''. | * In the {{button|text=Input Vector Data List}} choose a vector polygon file with training areas: '''lab07_training_input.shp'''. | ||
* Activate the checkbox {{button|text=Validation Vector Data List}} and choose a vector polygon file with an independent sample of validation areas: '''lab07_validation_input.shp''' | * Activate the checkbox {{button|text=Validation Vector Data List}} and choose a vector polygon file with an independent sample of validation areas: '''lab07_validation_input.shp''' | ||
| − | * | + | * In the {{button|text=Output model}} specify an output model file: e.g. '''svm.model''' |
| − | * Set {{button|text= | + | * Activate the checkbox and save the {{button|text=Output confusion matrix or contingency table}} as '''ConfusionMatrixSVM.csv'''. |
| − | * | + | * 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 {{button|text=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 {{button|text=LibSVM classifier}} from the drop down list as Classifier to use for the training. | ||
| + | * The SVN Kernel Type is {{button|text=Gaussian radial basis function}}. | ||
| + | * Switch the Parameters optimization to {{button|text=on}}. | ||
| + | * Set user defined seed with an integer value. | ||
* Click on {{button|text=Execute}}. | * Click on {{button|text=Execute}}. | ||
| − | + | [[File:qgis_trainImagesClassifier_svm.png|500px]] | |
== Classification== | == Classification== | ||
Revision as of 19:18, 8 December 2018
Contents |
Classification with Orfeo Toolbox
Training of 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 0.
- 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 SVN 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
- Open Orfeo Toolbox --> Image Classification (see figure C).
- Set Subset_S2A_MSIL2A_20170619T_MUL_BOA.tif as Input image.
- Set SVM.model as Model file.
- Set Subset_S2A_MSIL2A_20170619T_MUL_BOA.xml as Statistical file.
- Save the Output image as su_svm.tif.
- Evaluate classification results.
- Add the classification result su_svm.tif to QGIS.
- Right click su_svm.tif in the TOC and select Properties --> Style --> Style --> Load Style.
- Load lab05_MinDist.qml.
Compute a confusion matrix with independent reference data
- Open Orfeo Toolbox --> ComputeConfusionMatrix (Vector).
- Set su_svm.tif as Input image.
- Set lab05_validation.shp as Input reference vector data.
- Set Field name to C_ID.
