TrainMiniBatchKMeans Task

This task executes an unsupervised Mini Batch K-Means algorithm against the provided input training rasters. The algorithm iterates between two major steps, first step, samples are drawn randomly from the dataset, to form a mini-batch. These are then assigned to the nearest centroid. In the second step, the centroids are updated.

For background on the algorithm used, see Mini Batch K-Means 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('TrainMiniBatchKMeans')

; 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('TrainExtraTrees')

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 Mini Batch K-Means 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