Revision as of 11:25, 10 December 2018

Mean-shift segmentation of large size images

• Perform a mean shift filtering on the input image Subset_S2A_MSIL2A_20170619T_BOA.tif. We extract homogeneous objects on the basis of a filtered image.

Go to Processing-Toolbox --> Orfeo Toolbox (image analysis) --> Image Filtering --> Exact Large-Scale Mean-Shift segmentation, step 1 (Smoothing).

1. 1. • Set the Subset_S2A_MSIL2A_20170619T_BOA layer as Input image.
      • Increase the Available RAM (Mb) according to your system to speed up the process.
      • The Range radius should be set to 30.
      • Leave all other configurations as they are and click Run. Have a look on the resulting filtered and spatial images.
2. Step. Convert the filtered image to one band image object. Go to Processing-Toolbox --> Orfeo Toolbox (image analysis) --> Segmentation --> Exact Large-Scale Mean-Shift segmentation, step 2.
   1. • The Filtered image is Filter output from step 1.
      • Leave the Spatial image as [not selected].
      • Range radius is set to 30.
      • Define a path to the Directory where to write temporary files.
      • Leave all other configurations as they are and click Run. The Output image is a labeled image where neighbor pixels whose range distance is below range radius will be grouped together into the same cluster.
3. Step. Adjust the image object size merging small regions with the module Processing-Orfeo Toolbox (image analysis) --> Segmentation-Exact --> Large-Scale Mean-Shift segmentation, step 3 (optional).
   1. • The Input image is the Subset_S2A_MSIL2A_20170619T_BOA layer.
      • The Segmented image is Output image from step 2.
      • The Minimum Region size is set to 10.
      • Leave all other configurations as they are and click Run.
4. Step. Convert the image segments from raster to polygon vectors. Label ID, mean and standard deviation, number of the pixels in an image polygon are calculated and added as additional columns in the resulting shapefile.Processing-Orfeo Toolbox (image analysis) --> Segmentation-Exact Large-Scale Mean-Shift segmentation, step 4
   1. • The Input image is the Subset_S2A_MSIL2A_20170619T_BOA layer.
      • Segmented image is Output image from step 3.
      • The name of Output GIS vector file is seg_Subset_S2A_MSIL2A_20170619T_BOA.shp
      • For the rest of parameters keep default values.Run.

Change Layer --> Properties Style --> Fill Style to No Brush and Colors --> Border to white. Overlay the vector file on top of the image Subset_S2A_MSIL2A_20170619T_BOA.tif to evaluate the segmentation result.

Preparation of reference data

Join the Land use/cover (LUC) class attribute of manually digitized training areas with the image segments. Vector --> Data Management Tools --> Join Attributes by Location ....

1. 1. • The Target vector file is the Output GIS vector file of step 4.
      • Join vector layer is training_manual_poly.shp.
      • Output vector seg_Subset_S2A_MSIL2A_20170619T_BOA_train.shp.
      • For the rest of parameters keep default values.OK.

Rename the field names of the resulting output vector attribut table avoiding upper capitals and underline using the Table Manager plugin. Rename the attribut field C_ID to class.

Object-based classifcation using SVM algorithm

We may use the windows command line and OTB QT graphical user interfaces. Start --> All Programs --> OSGeo4W --> OSGeo4W Shell or double click on the file C:\OSGeo4W64\OSGeo4W.bat.

1. A windows command shell opens, please type otbgui_ComputeOGRLayersFeaturesStatistics.
   1. • Name of the input vector is seg_Subset_S2A_MSIL2A_20170619T_BOA_train.shp.
      • XML file containing mean and variance of each feature is seg_Subset_S2A_MSIL2A_20170619T_BOA_train.xml.
      • In the List of features to consider for statistics mark the columns meanB0, meanB1, meanB2.Execute. Calculate also an XML file for vector file seg_Subset_S2A_MSIL2A_20170619T_BOA.shp.
2. Type into the OSGeo4W shell otbgui_TrainOGRLayersClassifier.
   1. • Name of the input shapefile is seg_Subset_S2A_MSIL2A_20170619T_BOA_train.shp.
      • XML file containing mean and variance of each feature is seg_Subset_S2A_MSIL2A_20170619T_BOA_train.xml.
      • Output model filename is seg_Subset_S2A_MSIL2A_20170619T_BOA_train.model
      • In the List of features to consider for statistics mark the columns meanB0, meanB1, meanB2, ...
      • The name ofField containing the class id for supervision" is class.Execute.
3. Type into the OSGeo4W shell otbgui_OGRLayerClassifier.
   1. • Name of the input shapefile is seg_Subset_S2A_MSIL2A_20170619T_BOA.shp.
      • XML file containing mean and variance of each feature is seg_Subset_S2A_MSIL2A_20170619T_BOA.xml.
      • Input model filename is seg_Subset_S2A_MSIL2A_20170619T_BOA_train.model
      • In the List of features to consider for statistics mark the columns meanB0, meanB1, meanB2, ...
      • The name ofField containing the predicted class" is predicted.Execute.
4. Open the resulting vector file in a QGIS map view.
   1. • Change the style of the classified vector layer: Go to layer properties --> style. Click on ‘Load Style’. Browse for OBIA_legend file and select OBIA_legend.qml}}. apply, OK.
5. Simplify the vector file using Vector --> Geoprocessing Tools --> Dissolve...
   1. • Input vector layer is seg_Subset_S2A_MSIL2A_20170619T_BOA.shp.
      • Dissolve field is predicted.
      • Output shapefile is seg_188_Subset_S2A_MSIL2A_20170619T_BOA_diss.shp

Info

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