Latest revision as of 18:25, 8 December 2018

[edit] Image statistics(not mandatory, only if normalization of the variables is required)

• 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 ComputeImagesStatistics and double click ComputeImagesStatistics.
• Specify a multispectral image as Input Image: the Sentinel-2 image Subset_S2A_MSIL2A_20170619T_MUL_BOA.tif
• Specify directory and name for the XML Output image. Specify the extension .xml for this file.
• Click on Execute.

[edit] Classification with Orfeo Toolbox (Outdated Qgis 2)

[edit] Image statistics

1. Add the Sentinel-2 imagery Subset_S2A_MSIL2A_20170619T_MUL_BOA.tif into a QGIS project.
2. Calculate mean and standard error for each band of the Sentinel-2 imagery using the OTB Graphical User Interface.

• In the Search box on the Windows Start menu type OSGeo4W Shell. You should be able to open the shell by clicking on it.
• Type into the shell: otbgui_ComputeImagesStatistics.

Select a multiband input file and an output XML file as seen in the screenshot below.

[edit] Train image classifier

1. Add the training areas as vector polygon file lab05_training_input.shp into QGIS.
2. Open Orfeo Toolbox --> TrainImageClassifier (libsvm) to use the Support Vector Machine SVM algorithm (see figure B).
3. Set Subset_S2A_MSIL2A_20170619T_MUL_BOA.tif as Input image list.
4. Set lab05_training_input.shp as Input vector list.
5. Set Subset_S2A_MSIL2A_20170619T_MUL_BOA.tif.xml as Input XML image statistics file.
6. Set Name of discrimination field to C_ID (C_ID refers to the column that contains the LUC code).
7. Save the Output confusion matrix as ConfusionMatrixSVM.csv.
8. Save the Output model as SVM.model.
9. Calculation of accuracies :
   Open ConfusionMatrixSVM.csv in LibreOffice or MS Excel and calculate overall, producer and consumer accuracies.

[edit] Classification

1. Open Orfeo Toolbox --> Image Classification (see figure C).
2. Set Subset_S2A_MSIL2A_20170619T_MUL_BOA.tif as Input image.
3. Set SVM.model as Model file.
4. Set Subset_S2A_MSIL2A_20170619T_MUL_BOA.xml as Statistical file.
5. Save the Output image as su_svm.tif.
6. Evaluate classification results.
   1. Add the classification result su_svm.tif to QGIS.
   2. Right click su_svm.tif in the TOC and select Properties --> Style --> Style --> Load Style.
   3. Load lab05_MinDist.qml.

[edit] Compute a confusion matrix with independent reference data

1. Open Orfeo Toolbox --> ComputeConfusionMatrix (Vector).
2. Set su_svm.tif as Input image.
3. Set lab05_validation.shp as Input reference vector data.
4. Set Field name to C_ID.

[edit] GRASS implementation

When testing for GRASS in the Semi-Automatic Classification Plugin's Settings tab, I needed to have grass set up first (i.e. created a grassdata folder with a location and mapset); this makes sense, but I don't know if we provide a tutorial for this yet? - Levent (talk) 17:22, 24 February 2014 (CET)

This Module does not work in QGIS 2.18-ltr:

1. 1. Open Orfeo Toolbox --> Compute images second order statistics (see figure A).
   2. Set Subset_S2A_MSIL2A_20170619T_B12_BOA.tif as Input images.
   3. Save the Output XML file as Subset_S2A_MSIL2A_20170619T_B12_BOA.tif.xml.

Outdated figures:

[edit] Figures

Figure A: Dialogues of the Compute images second order statistics	Figure B: Dialogues of the TrainImageClassifier (libsvm)
Figure C: Dialogues of the Image Classification	Figure D: Dialogues of the ComputeConfusionMatrix (Vector)