Object-based supervised classification

Object-based image analysis (OBIA) with QGIS and OTB processing plugin

Segmentation

• In the search engine of Processing Toolbox, type segmentation and double click Segmentation.
• Set Segmentation algorithm to meanshift
• Select the input image: Subset_S2A_MSIL2A_20170619T_MUL.tif (data type uint 16bit).
• The Range radius value can be set to 600. The optimal value depends on datatype dynamic range of the input image and requires experimental trials for the specific classifcation objectives.
• Set Minimum Region size (in pixels) to 16.
• Processing mode Vector
• Set the Mask image to blank (top of dro-down list).
• The Minimum Segment size (in pixels) can be set to 16 depending on minimum mapping size.
• Check 8-neighborhood connectivity on.
• Change Output pixel type to uint32.
• Name the Output vector file e.g. segments_meanshift_seg.gpkg (GeoPackage).
• Click Run.

• Evaluate the segmentation results: Load the output vector file segments_meanshift.gpkg into QGIS on top of the image Subset_S2A_MSIL2A_20170619T_Mul.tif

Mark the vector layer in the Qgis Layers window. Layer --> Properties --> Symbology --> Simple Fill, Fill Style: No Brush and Stroke color:white.

Feature extraction

In the search engine of Processing Toolbox, type zonalstats and open ZonalStatistics under Image Manipulation of OTB.

• Select the Input image: Subset_S2A_MSIL2A_20170619T_MUL.tif.
• Background value to ignore: 0
• For the Input vector data do not select a vector file from the file list which are already loaded in the QGIS Viewer. There is currently a bug in QGIS 3.16 which leads to failure because the full pathname is not parsed correctly. Instead, please select a vector file by clicking and browse directly to the file containing the result from Segmentation: segment polygons in format GPKG segments_meanshift.gpkg.
• File name for the output vector data: segments_meanshift_zonal.gpkg.
• Click Run.

Training phase

• In the search engine of Processing Toolbox, type Train and double click TrainVectorClassifier.
• In the Input Vector Data List do not select a file from the list which is 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 e.g. lucc_training_obia.gpkg.
• Output model filename is svm_obia.model
• In the field Field names for training features copy and paste

 "mean_2  stdev_0  mean_9  mean_7  mean_0" 

• The name of Field containing the class id for supervision" is C_ID.
• Classifier to use for training: libsvm
• SVM Kernel Type: linear
• SVM Model Type: csvc
• Click Parameters optimizationON.
• Click Run.

Info

For more detailed information on the SVM algorithm visit the LibSVM website

Classification phase

• In the search engine of Processing Toolbox, type Vector and double click VectorClassifier.
• In the Input Vector Data do not select a file from the list which is 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 segments and features for the whole image (result of Feature extaction)training area polygons in format GPKG segments_meanshift_zonal.gpkg.
• Name of the input model file is svm_obia.model.
• Output field containing the class is C_ID
• Copy and paste into the field Field names to be calculated have to be the same features for prediction as were defined before in the TrainVectorClassifier module:

 "mean_0 mean_1 stdev_1 mean_2 mean_3 mean_4 mean_5 mean_6 stdev_6 mean_8 stdev_8 mean_9" 

• Output vector Data file is lucc_classified_obia.gpkg.

Run.

Load the output vector file manually into QGIS. Layer --> Layer properties --> Symbology > Style --> Load style.... Open the same QGIS style file which is already in use for the training data: lucc_training_obia.qml.