The Local Outlier Factor Classification tool executes the local outlier factor anomaly detection algorithm against the provided input raster, then performs classification. It detects the samples that have a substantially lower density than its neighbors and labels the detections as anomalies.

This tool performs anomaly detection 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 anomaly detection using the model in the Machine Learning Classification Tool, or build a workflow in the ENVI Modeler.

For background on the algorithm used, see Local Outlier Factor Classification.

  1. From the Toolbox, select Machine Learning > Anomaly > Local Outlier Factor ClassificationIn . The Isolation Forest Classification dialog appears.
  2. Select an Input Raster, perform optional spatial and spectral subsetting and/or masking, then click OK.
  3. In the Input ROIs field, 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.
  4. Specify the ROI classes to use as background in the Background Labels field. These indicate classes of no interest.
  5. Specify the Leaf Size in the field provided. Changing the leaf size can affect the speed of construction and query, as well as the memory required to store the tree. The optimal value depends on the nature of the problem.
  6. In the Output Raster field, enter a location and filename for the classification raster.
  7. Enable the Display result check box to display the output in the view when processing is complete.

  8. 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.

  9. 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.

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

  11. Click OK.

See Also

ENVI Machine Learning Algorithms Background, TrainLocalOutlierFactor Task, Isolation Forest Classification Tool, Machine Learning Labeling Tool