Use the Build Deep Learning Raster tool to build a raster than can be used for classification by a deep learning model. Specifically, it creates a raster with a byte data type and whose pixel values are stretched between a specified minimum and maximum value. If the input raster is already byte data and you do not specify minimum or maximum values, the raster will not be stretched.

You can also write a script to build a deep learning raster using the BuildDeepLearningRaster task.

Follow these steps:

  1. In the ENVI Toolbox, select Deep Learning > Pixel Segmentation > Build Deep Learning Raster.
  2. In the Input Raster field, specify a raster that you want to classify with a deep learning model.
  3. In the Input Model field, specify the TensorFlow model in HDF5 format (.h5) that will be used with the deep learning raster for classification.
  4. Optional: Specify a Minimum pixel value to be considered, also known as the black point. This value will apply to all bands in the raster. Or, you can specify an array of values, one per band. An example is [0,1,0,1] for a four-band raster. If you do not specify a minimum value, the minimum value of the input raster will be used for stretching across all bands.
  5. Optional: Specify a Maximum pixel value to be considered, also known as the white point. This value will apply to all bands in the raster. Or, you can specify an array of values, one per band. An example is [254,255,254,255] for a four-band raster. If you do not specify a maximum value, the maximum value of the input raster will be used for stretching across all bands.
  6. In the Output Raster field, select a location and filename for the deep learning 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 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.

  9. 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.

  10. Click OK. ENVI adds the resulting output to the Data Manager and Layer Manager.

See Also

TensorFlow Pixel Classification