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.
• Select the input image: a multispectral Sentinel-2 image (data type uint16).
• Set Segmentation algorithm to meanshift
• 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 drop-down list).
• Check 8-neighborhood connectivity on.
• The Minimum object size (in pixels) can be set to 16 depending on minimum mapping size.
• Name the Output vector file e.g. segments_meanshift.shp. Extension should be .shp in this module).
• Click Run.

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

Mark the vector layer in the Qgis Layers window. Right click 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: a multispectral Sentinel-2 image.
• Background value to ignore: 65535
• For the Input vector data select a vector file with segments (output from Segmentation)
• File name for the output vector data: segments_stats.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" 

• This is one of many variable sets as a result of a feature selection procedure.
• 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 VectorClass 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_2 stdev_0 mean_9 mean_7 mean_0" 

• Output vector Data file is lucc_classified_obia.gpkg.

Run.

Load the output vector file manually into QGIS and apply the same QGIS style used for the training data. Layer --> Layer properties --> Symbology > Style --> Load style....