BuildLabelRasterFromClassification Task

This task creates a label raster from an input raster and binary classification raster, which can be used to train a model. 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 in the input raster correspond to a feature of interest.

All images used for training 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 calling the BuildLabelRasterFromClassification task. Note that while the BuildLabelRasterFromClassification 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

This example calculates a Normalized Difference Vegetation Index (NDVI) on a multispectral image. It applies a threshold to the highest NDVI values and creates a binary classification image from the thresholded values. The classification image is used to create a label raster that can be used to train a deep learning model to identify green vegetation.

; Start the application

e = ENVI()

; Open an input image

File = Filepath('qb_boulder_msi', SUBDIR=['data'], $

  ROOT_DIR=e.Root_Dir)

Raster = e.OpenRaster(File)

; Calculate NDVI

SITask = ENVITask('SpectralIndices')

SITask.INPUT_RASTER = Raster

SITask.INDEX = 'NDVI'

SITask.Execute

; Apply color slice classification

ClassTask = ENVITask('ColorSliceClassification')

ClassTask.INPUT_RASTER = SITask.OUTPUT_RASTER

ClassTask.CLASS_COLORS = [0,128,0]

ClassTask.CLASS_RANGES = [0.3, 1.0]

ClassTask.Execute

; Get the task from the catalog of ENVITasks

Task = ENVITask('BuildLabelRasterFromClassification')

; Define inputs

Task.INPUT_RASTER = Raster

Task.INPUT_CLASSIFICATION_RASTER = ClassTask.OUTPUT_RASTER

; 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('BuildLabelRasterFromClassification')

Input parameters (Set, Get): CLASS_NAMES, ENHANCE_DISPLAY, INPUT_CLASSIFICATION_RASTER, INPUT_RASTER, 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 classification raster does 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_CLASSIFICATION_RASTER (required)

Specify a binary classification raster that provides labeled pixel data for the desired class in the input raster. You can create the classification raster using any of ENVI's tools to create single-class output. With a binary classification raster, pixel values of 1 represent the feature of interest and values of 0 represent the background. The classification raster must have the same dimensions as the input raster.

INPUT_RASTER (required)

Specify the input raster that will be used as label data.

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 and byte data type.

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