ClassActivationToPixelROI Task

This task creates pixel regions of interest (ROIs) from a class activation raster generated by a deep learning model.

This task is part of ENVI Deep Learning, which requires a separate license and installation.

The ROIs created with this tool can be combined with the original ROIs to create new label rasters to either train a new model again or to refine a trained model. Pixel ROIs do not contain georeferencing information. They can only be used as input to creating label rasters that are perfectly coregistered with their associated input rasters. If you want to create ROIs that can reproject to a georeferenced raster, use the ClassActivationToPolygonROI task instead.

Example

The file TrainedModelContainers.h5 is included with the ENVI Deep Learning tutorial data, which is available on our ENVI Tutorials web page. Click the "Deep Learning" link in the ENVI Tutorial Data section to download a zip file containing the data. Extract the contents to a local directory. That file is in the shipping_containers directory.

; Start the application

e = ENVI()

; Open an input file

File = Filepath('OaklandPortOrthophoto1.dat', $

  Subdir=['data','deep_learning'], Root_Dir=e.Root_Dir)

Raster = e.OpenRaster(File)

ModelFile = 'C:\MyTutorialFiles\TrainedModelContainers.h5'

Model = ENVITensorFlowModel(ModelFile)

; Run the classification task

ClassTask = ENVITask('TensorFlowPixelClassification')

ClassTask.INPUT_RASTER = Raster

ClassTask.INPUT_MODEL = Model

ClassTask.OUTPUT_CLASS_ACTIVATION_RASTER_URI = e.GetTemporaryFilename()

ClassTask.Execute

; Get the task from the catalog of ENVITasks

Task = ENVITask('ClassActivationToPixelROI')

; Define inputs

Task.INPUT_RASTER = ClassTask.OUTPUT_CLASS_ACTIVATION_RASTER

; Run the task

Task.Execute

; Add the output to the Data Manager

e.Data.Add, ClassTask.OUTPUT_CLASS_ACTIVATION_RASTER

; Display the result

View = e.GetView()

Layer = View.CreateLayer(Raster)

roiLayer = Layer.AddRoi(Task.OUTPUT_ROI)

Syntax

Result = ENVITask('ClassActivationToPixelROI')

Input properties (Set, Get): INPUT_RASTER, METHOD, OUTPUT_ROI_URI, THRESHOLD

Output properties (Get only): OUTPUT_ROI, OUTPUT_THRESHOLD

Properties marked as "Set" are those that you can set to specific values. You can also retrieve their current values any time. Properties marked as "Get" are those whose values you can retrieve but not set.

Methods

This task inherits the following methods from ENVITask. See the ENVITask topic in ENVI Help.

AddParameter
Execute
Parameter
ParameterNames
RemoveParameters

Properties

This task inherits the following properties from ENVITask:

COMMUTE_ON_DOWNSAMPLE

COMMUTE_ON_SUBSET

DESCRIPTION

DISPLAY_NAME

NAME

REVISION

See the ENVITask topic in ENVI Help for details.

This task also contains the following properties:

INPUT_RASTER (required)

Specify an input class activation raster.

METHOD (optional)

Specify the thresholding method to use to differentiate feature and background pixels in INPUT_RASTER. This property only applies to a single feature class and is ignored for multiple feature classes. If THRESHOLD is specified, it will supersede METHOD. The following are valid values.

Isodata
Mean
Maximum Entropy
Minimum Error
Moments
Otsu (default)

OUTPUT_ROI

This is a reference to the output ROI.

OUTPUT_ROI_URI (optional)

Specify a string with the fully qualified path and filename for OUTPUT_ROI.

OUTPUT_THRESHOLD

If THRESHOLD is not specified, then this will be the threshold calculated by METHOD. If THRESHOLD is specified, then this will be the threshold specified.

THRESHOLD (optional)

Specify a threshold value to use to differentiate feature and background pixels in INPUT_RASTER. If the highest feature class value for a given pixel is greater than or equal to the threshold value, the pixel will be designated as that feature class. Otherwise, it will be designated as background. If you set the THRESHOLD property, that will supersede METHOD.

Version History

Deep Learning 1.0	Introduced
Deep Learning 1.1	Modified METHOD and THRESHOLD properties to allow multiple classes.