Supervised classification (Tutorial)

Classification with Orfeo Toolbox

Image statistics

1. Add the LandSat-8 imagery SUB_LC81950242013188LGN00_MUL.tif into a QGIS project. It should be available in the course data.
2. Calculate mean and standard error for each band of the LandSat-8 imagery.
   1. Open Orfeo Toolbox --> Compute images second order statistics (see figure A).
   2. Set SUB_LC81950242013188LGN00_MUL.tif as Input images.
   3. Save the Output XML file as SUB_LC81950242013188LGN00_mul.tif.xml.

Train image classifier

1. Add the training areas training_manual_poly.shp into QGIS. It should be available in the course data.
2. Open Orfeo Toolbox --> TrainImageClassifier (libsvm) to use the Support Vector Machine SVM algorithm (see figure B).
3. Set SUB_LC81950242013188LGN00_MUL.tif as Input image list.
4. Set training_manual_poly.shp as Input vector list.
5. Set SUB_LC81950242013188LGN00_MUL.tif.xml as Input XML image statistics file.
6. Set Name of discrimination field to C_ID (C_ID refers to the column that contain the LUC classes 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.

Classification

1. Add the cloud mask cloud_shadow_mask.tif into QGIS. It should be available in the course data.
2. Open Orfeo Toolbox --> Image Classification (see figure C).
3. Set SUB_LC81950242013188LGN00_MUL.tif as Input image.
4. Set cloud_shadow_mask.tif as Input mask.
5. Set SVM.model as Model file.
6. Set SUB_LC81950242013188LGN00_MUL.tif.xml as Statistical file.
7. Save the Output image as su_svm.tif.
8. 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 classifcation.qml, which should be available in the course data.
   4. Open the Google Maps Layer under Web --> ObenLayers plugin --> Add Google Satellite layer and look for misclassification.

Compute a confusion matrix with independent reference data

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

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)