Use the Subset Label Raster tool to build a new deep learning label raster using a subset of existing labels in another raster. This tool is useful for removing confusing classes or reusing previously-labeled data for a new project.

You can also write a script to build a subset label raster using the SubsetLabelRaster task.

Follow these steps:

  1. In the ENVI Toolbox, select Deep Learning > Subset Label Raster
  2. In the Input Raster field, specify an input raster containing labels of interest. All ENVI Deep Learning rasters, pixel segmentation, and object detection rasters are supported.
  3. In the Output Labels field, specify a subset of existing labels from the input raster that will be applied to the output raster.
  4. In the Output Raster field, select a location and filename for the deep learning 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 next to the OK button 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 in the background, click the down arrow and select Run Task in the Background. If an ENVI Server has been set up on the network, the Run Task on remote ENVI Server name is also available. 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. Click OK. ENVI adds the resulting output to the Data Manager and Layer Manager.

See Also

Train TensorFlow Pixel Models, Train TensorFlow Object Models, TrainTensorFlowGridModel Task