BuildLabelRasterFromROI Task

This task creates a label raster from an input raster and regions of interest (ROIs) that correspond to a specific feature. The output label raster contains the original image bands plus an additional band that consists of a binary mask. The mask band indicates which pixels are in the ROI, which identifies the feature of interest.

All images used for training and classification should have the same data type (byte) and range of data values. For better results, use the BuildDeepLearningRaster task with the same minimum and maximum pixel values for all images prior to using the BuildLabelRasterFromROI task. Note that while this task will do this conversion for you when images are not of byte data type, it converts each image with its own minimum and maximum, which is not as optimal as a single minimum and maximum for all images.

Example

Sample data files are 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. Files are located in the tornado directory.

; Start the application

e = ENVI()

; Update the following line with the correct path

; to the tutorial data files

File = 'C:\MyTutorialFiles\TrainingRaster1.dat'

Raster = e.OpenRaster(File)

; Update the following line with the correct path

; to the tutorial data files

ROIFile = 'C:\MyTutorialFiles\TrainingRaster1ROIs.xml'

ROI = e.OpenROI(ROIFile)

; Get the task from the catalog of ENVITasks

Task = ENVITask('BuildLabelRasterFromROI')

; Define inputs

Task.INPUT_RASTER = Raster

Task.INPUT_ROI = ROI

Task.CLASS_NAMES = ['Roof Damage', $

  'Structural Damage', 'Rubble', 'Blue Tarps']

; Run the task

Task.Execute

; Add the output to the Data Manager

e.Data.Add, Task.OUTPUT_RASTER

; Display the result

View = e.GetView()

Layer = View.CreateLayer(Task.OUTPUT_RASTER)

Syntax

Result = ENVITask('BuildLabelRasterFromROI')

Input parameters (Set, Get): CLASS_NAMES, ENHANCE_DISPLAY, INPUT_RASTER, INPUT_ROI, OUTPUT_RASTER_URI, VISUAL_RGB

Output parameters (Get only): OUTPUT_RASTER

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.

Input Parameters

CLASS_NAMES (optional)

Specify a string array with a list of class names that the model will be trained to find. If the input ROIs do not contain all these classes, or if they are out of order, this parameter will ensure consistency between tasks.

ENHANCE_DISPLAY (optional)

Specify whether to apply an additional small stretch to the processed data to suppress noise and enhance feature visibility. The optional stretch is effective for improving visual clarity in imagery acquired from aerial platforms or sensors with higher noise profiles.

INPUT_RASTER (required)

Specify the input raster to use for label data.

INPUT_ROI (required)

Specify an ROI that indicates the labeled pixels for the desired class in the input raster.

In some cases, you may want to create a label raster that consists entirely of background pixels with no feature pixels. To do this, create an empty ENVIROI object and use it for INPUT_ROI; for example:

roi = ENVIROI(NAME='empty', color=[0,255,128])

Task.INPUT_ROI = roi

OUTPUT_RASTER_URI (optional)

Specify a string with the fully qualified filename and path of the associated OUTPUT_RASTER. If you do not specify this property, or set it to an exclamation symbol (!), a temporary file will be created.

VISUAL_RGB (optional)

Specify whether to encode the output raster as a three-band RGB composite (red, green, blue) for color image processing. This ensures consistent band selection from ENVI display types (such as RGB, CIR, and pan) and supports integration of diverse data sources (such as MSI, panchromatic, and VNIR) without band mismatch.

Output Parameters

OUTPUT_RASTER

This is a reference to the output raster of filetype ENVI.

If INPUT_RASTER is not byte data then each band will be byte stretched to its minimum and maximum.

Methods

Execute

Parameter

ParameterNames

See ENVI Help for details on these ENVITask methods.

Properties

DESCRIPTION

DISPLAY_NAME

NAME

REVISION

Version History

Deep Learning 1.0	Introduced
Deep Learning 1.1	Added CLASS_NAMES parameter