ArcMap

Train ISO Cluster Classifier

Summary

Generate an Esri classifier definition (.ecd) file using the Iso Cluster classification definition.

This tool performs an unsupervised classification.

Usage

  • Any Esri-supported raster is accepted as input, including raster products, segmented raster, mosaics, image services, or generic raster datasets. Segmented rasters must be 8-bit rasters with 3-bands.

  • The Segment Attributes parameter is enabled only if one of the raster layer inputs is a segmented image.

Syntax

TrainIsoClusterClassifier (in_raster, max_classes, out_classifier_definition, {in_additional_raster}, {max_iterations}, {min_samples_per_cluster}, {skip_factor}, {used_attributes}, {max_merge_per_iter}, {max_merge_distance})
ParameterExplanationData Type
in_raster

Select the raster dataset you want to classify.

Raster Layer; Mosaic Layer; Image Service; String
max_classes

Maximum number of desired classes to group pixels or segments. This should be set to be greater than the number of classes in your legend.

It is possible that you will get fewer classes than what you specified for this parameter. If you need more, increase this value and aggregate classes after the training process is complete.

Long
out_classifier_definition

This is a JSON file that contains attribute information, statistics, hyperplane vectors and other information needed for the classifier. A file with an .ecd extension is created.

File
in_additional_raster
(Optional)

Optionally incorporate ancillary raster datasets, such as a multispectral image or a DEM, to generate attributes and other required information for classification.

Raster Layer; Mosaic Layer; Image Service; String
max_iterations
(Optional)

The maximum number of iterations for the clustering process to run.

The recommended range is between 10 and 20 iterations. Increasing this value will linearly increase the processing time.

Long
min_samples_per_cluster
(Optional)

The minimum number of pixels or segments in a valid cluster or class.

The default value of 20 has shown to be effective in creating statistically significant classes. You can increase this number to have more robust classes; however, it may limit the overall number of classes that are created.

Long
skip_factor
(Optional)

Number of pixels to skip for a pixel image input. If a segmented image is an input, specify the number of segments to skip.

Long
used_attributes
[used_attributes;used_attributes,...]
(Optional)

Specify the attributes to be included in the attribute table associated with the output raster.

  • COLOR —The RGB color values are derived from the input raster, on a per-segment basis.
  • MEAN —The average digital number (DN), derived from the optional pixel image, on a per-segment basis.
  • STD —The standard deviation, derived from the optional pixel image, on a per-segment basis.
  • COUNT —The number of pixels comprising the segment, on a per-segment basis.
  • COMPACTNESS —The degree to which a segment is compact or circular, on a per-segment basis. The values range from 0 to 1, where 1 is a circle.
  • RECTANGULARITY —The degree to which the segment is rectangular, on a per-segment basis. The values range from 0 to 1, where 1 is a rectangle.

This parameter is only enabled if the Segmented key property is set to true on the input raster. If the only input into the tool is a segmented image, the default attributes are COLOR, COUNT, COMPACTNESS, and RECTANGULARITY. If an in_additional_raster is also included as an input along with a segmented image, then MEAN and STD are available as options.

String
max_merge_per_iter
(Optional)

Increasing the number of merges will reduce the number of classes that are created. A lower value will result in more classes.

Long
max_merge_distance
(Optional)

Increasing the distance will allow more clusters to merge, resulting in fewer classes. A lower value will result in more classes.

This is the distance between the cluster centers in feature space. Although you can set this to any value you wish, values from 0 to 5 tend to give the best results.

Double

Code sample

TrainIsoClusterClassifier example 1 (Python window)

The following Python window script uses the ISO Cluster classifier to create an unsupervised Esri classification definition file with a maximum of ten classes.

import arcpy
from arcpy.sa import *

TrainIsoClusterClassifier("c:/test/moncton_seg.tif", "10", 
                "c:/output/moncton_sig_iso.ecd","c:/test/moncton.tif", 
                "5", "10", "2", "COLOR;MEAN;STD;COUNT;COMPACTNESS;RECTANGULARITY")
TrainIsoClusterClassifier example 2 (stand-alone script)

This script example uses the ISO Cluster classifier to create an unsupervised Esri classification definition file with a maximum of ten classes.

# Import system modules
import arcpy
from arcpy.sa import *


# Set local variables
inSegRaster = "c:/test/moncton_seg.tif"
maxNumClasses = "10"
out_definition = "c:/output/moncton_sig_iso.ecd"
in_additional_raster = "moncton.tif"
maxIteration = "20"
minNumSamples = "10"
skipFactor = "5"
attributes = "COLOR;MEAN;STD;COUNT;COMPACTNESS;RECTANGULARITY"

# Check out the ArcGIS Spatial Analyst extension license
arcpy.CheckOutExtension("Spatial")

# Execute 
TrainIsoClusterClassifier(inSegRaster, maxNumClasses, out_definition,
                          in_additional_raster, maxIteration, 
                          minNumSamples, skipFactor, attributes)

Environments

Licensing information

  • ArcGIS Desktop Basic: Requires Spatial Analyst
  • ArcGIS Desktop Standard: Requires Spatial Analyst
  • ArcGIS Desktop Advanced: Requires Spatial Analyst

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