The Machine Learning Classification tool performs classification for all ENVI Machine Learning model types.

You can also write a script to perform classification for all ENVI Machine Learning model types using the MachineLearningClassification task.

  1. From the Toolbox, select Machine Learning > Machine Learning Classification. The Machine Learning Classification dialog appears.
  2. Select an Input Raster, perform optional spatial and spectral subsetting and/or masking, then click OK.
  3. In the Input Model field, select a trained machine learning model to use for classifying.

  4. In the Normalize field, you can optionally apply normalization statistics to the raster that will be used for classification by specifying Min and Max data values that correspond to 0% and 100% reflectance.

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

  7. 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.

  8. To run the process on a local or remote ENVI Server, click the down arrow and select Run Task in the Background or Run Task on remote ENVI Server name. The ENVI Server Job Console will show the progress of the job and will provide a link to display the result when processing is complete. See the ENVI Servers topic in ENVI Help for more information.

  9. Click OK.

See Also

Machine Learning Labeling Tool