ENVI Machine Learning Unsupervised Classification Tutorial

The Mini Batch K-Means Classification tool runs an unsupervised mini batch K-Means algorithm against the provided input training rasters. The algorithm iterates between two major steps, first step, samples are drawn randomly from the dataset, to form a mini-batch. These are then assigned to the nearest centroid. In the second step, the centroids are updated.

This tool performs unsupervised classification on a single raster. You provide an input raster and parameter settings to generate a classified raster. For more advanced options, you can use the Train Mini Batch K-Means Tool to create a training model using one or more rasters, then 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 Mini Batch K-Means Classification.

  1. From the Toolbox, select Machine Learning > Unsupervised > Mini Batch K-Means Classification. The Mini Batch K-Means Classification dialog appears.
  2. Select an Input Raster, perform optional spatial and spectral subsetting and/or masking, then click OK.
  3. Enter the Number of Classes to identify. The default is 3.
  4. In the Output Raster field, enter a location and filename for the classification raster.
  5. Enable the Display result check box to display the output in the view when processing is complete.

  6. 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.

  7. 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.

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

  9. Click OK.

See Also

ENVI Machine Learning Algorithms Background, Train Mini Batch K-Means Tool, TrainMiniBatchKMeans Task, BIRCH Classification Tool