Supervised classification (Tutorial)
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= Classification with Orfeo Toolbox = | = Classification with Orfeo Toolbox = | ||
| − | == Training of per-pixel | + | == Training of per-pixel classifiers == |
* 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 one (optional: several) multispectral images: ''Subset_S2A_MSIL2A_20170619T_MUL.tif ''. | + | * In the {{button|text=Input Image List}} click on {{button|text=+}} and select one (optional: several) multispectral images: '''Subset_S2A_MSIL2A_20170619T_MUL.tif '''. |
| − | * In the {{button|text=Input Vector 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: '''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''' | ||
* Set ''Subset_S2A_MSIL2A_20170619T_MUL_BOA.tif.xml'' as {{button|text=Input XML image statistics file}}. | * Set ''Subset_S2A_MSIL2A_20170619T_MUL_BOA.tif.xml'' as {{button|text=Input XML image statistics file}}. | ||
* Set {{button|text=Name of discrimination field}} to ''C_ID'' (C_ID refers to the column that contains the LUC code). | * Set {{button|text=Name of discrimination field}} to ''C_ID'' (C_ID refers to the column that contains the LUC code). | ||
Revision as of 17:43, 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 one (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
- Set Subset_S2A_MSIL2A_20170619T_MUL_BOA.tif.xml as Input XML image statistics file.
- Set Name of discrimination field to C_ID (C_ID refers to the column that contains the LUC code).
- Save the Output confusion matrix as ConfusionMatrixSVM.csv.
- Save the Output model as SVM.model.
- Click on Execute.
- Calculation of accuracies :
Open ConfusionMatrixSVM.csv in LibreOffice or MS Excel and calculate overall, producer and consumer accuracies.
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.
