This task executes an unsupervised BIRCH algorithm against the provided input training rasters. This memory-efficient algorithm constructs a tree data structure with the cluster centroids being read off the leaf.

For background on the algorithm used, see BIRCH Classification.

Example

; Start the application
e = ENVI()
 
; Open an input raster file
RasterFile = Filepath('qb_boulder_msi', Subdir=['data'], $
Root_Dir=e.Root_Dir)
Raster = e.OpenRaster(RasterFile)
 
; Get the statistics task from the catalog of ENVITasks
StatsTask = ENVITask('NormalizationStatistics')
 
; Define inputs
StatsTask.INPUT_RASTERS = Raster
 
; Run the task
StatsTask.Execute
 
; Get the training task from the catalog of ENVITasks
TrainTask = ENVITask('TrainBIRCH')
 
; Define inputs
TrainTask.INPUT_RASTER = Raster
TrainTask.Number_of_Classes = 3
TrainTask.Normalize_Min_Max = StatsTask.Normalization
 
; Run the task
TrainTask.Execute
outputModel = TrainTask.OUTPUT_MODEL
Print, outputModel.Attributes

Syntax

Result = ENVITask('TrainBirch')

Input properties (Set, Get): BRANCHING_FACTOR, INPUT_RASTERS, MODEL_NAME, MODEL_DESCRIPTION, NORMALIZE_MIN_MAX, NUMBER_OF_CLASSES, OUTPUT_MODEL_URI, THRESHOLD

Output properties (Get only): OUTPUT_MODEL

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:

BRANCHING_FACTOR (optional)

Specify the maximum number of clustering feature subclusters in each node. The default is 50.

INPUT_RASTERS (required)

Specify one or more preprocessed training rasters to be used for training.

MODEL_NAME (optional)

Specify the name of the model. The default is BIRCH Unsupervised Classifier.

MODEL_DESCRIPTION (optional)

Specify the purpose of the model.

NORMALIZE_MIN_MAX (required)

Specify the data value that corresponds to 0% and 100% reflectance.

NUMBER_OF_CLASSES (optional)

Number of clusters after the final clustering step, which treats the subclusters from the leaves as new samples. Specifies the number of classes to identify. The default is 3.

OUTPUT_MODEL (required)

This is a reference to the output model file.

OUTPUT_MODEL_URI (optional)

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

THRESHOLD (optional)

Specify the radius of the subcluster, obtained by merging a new sample and the closest subcluster should be less than the threshold. The default is 0.5.

Version History

Deep Learning 2.0

Introduced

See Also

ENVI Machine Learning Algorithms Background, TrainExtraTrees Task, TrainIsolationForest Task, TrainKNeighbors Task, TrainLinearSVM Task, TrainLocalOutlierFactor Task, TrainMiniBatchKMeans Task, TrainNaiveBayes Task, TrainRandomForest Task, TrainRBFSVM Task