The RBF SVM Classification tool executes the Radial Basis Function Support Vector Supervised classification algorithm against the provided input raster, then performs classification. It leverages parameters gamma and C of the Radial Basis Function (RBF) kernel SVM.

This tool performs supervised classification on a single raster. You provide an input raster, ROIs, and parameter settings to generate a classified raster. For more advanced options, you can label data on one or more rasters in the Machine Learning Labeling Tool, train a model, and perform classification using the model in the Machine Learning Classification Tool, or build a workflow in the ENVI Modeler.

For background on the algorithm used, see RBF SVM Classification.

  1. From the Toolbox, select Machine Learning > Supervised > RBF SVM Classification. RBF SVM Classification dialog appears.
  2. Click the Browse button next to the Input Raster field. In the Data Selection dialog that appears, select the input raster, perform optional spatial and spectral subsetting and/or masking, then click OK.
  3. Click the Browse button next to the Input ROIs field. In the ROI Selection dialog that appears, click the Open File button and select an ROI file (.xml) that indicates the labeled pixels for the desired classes in the training raster (the ROIs must fall within the boundary of the input raster), then click OK. In the Input ROIs field, select one or more ROIs to use in the classification. You can also define ROIs on the raster displayed in the view by clicking the Open ROI Tool button .
  4. Specify the ROI classes to use as background in the Background Labels field. These indicate classes of no interest.

  5. For Remove Outliers, select Yes to remove outliers using histogram stretching to increase the minimum and decrease the maximum data values. Select No to use the true minimum and maximum data ranges to normalize the data.

  6. For Balance Classes, select Yes or No to specify whether all classes should be considered equal during training. Selecting Yes helps to account for classes with few samples compared to classes with many samples.

  7. In the Output Raster field, enter a location and filename for the classification raster.
  8. In the Output Model field, enter a location and filename for the model.
  9. Enable the Display result check box to display the output in the view when processing is complete.

  10. To reuse these task settings in future ENVI sessions, save them to a file. Click the down arrow and select Save Parameter Values, then specify the location and filename to save to. Note that some parameter types, such as rasters, vectors, and ROIs, will not be saved with the file. To apply the saved task settings, click the down arrow and select Restore Parameter Values, then select the file where you previously stored your settings.

  11. You can run the process in the background on your computer or in the background on an ENVI Server (if available). Click the down arrow next to the OK button and select one of the following:

    • Run Task in the Background: Runs the process on your computer. This method does not use an ENVI Server. The job progress will show in the ENVI Job Console's Background Jobs tab.
    • Run Task on envi_server_name. Runs the process on the selected ENVI Server. The job progress will show in the ENVI Job Console's envi_server_name tab.

    In ENVI Help, see ENVI Job Console for details on monitoring ENVI Server jobs and viewing results, and see ENVI Servers for details on setting up an ENVI Server.

  12. To see a model-based version of this tool that shows how the tool is constructed from individual tasks, click Open in Modeler.

  13. Click OK.

See Also

ENVI Machine Learning Algorithms Background, TrainRBFSVM Task, Extra Trees Classification Tool, K-Neighbors Classification Tool, Linear SVM Classification Tool, Naive Bayes Classification Tool, Random Forest Classification Tool