Train Maximum Likelihood Classifier (Spatial Analyst)—ArcGIS Pro

Available with Spatial Analyst license.

Available with Image Analyst license.

Summary

Generates an Esri classifier definition file (.ecd) using the Maximum Likelihood Classifier (MLC) classification definition.

Usage

To complete the maximum likelihood classification process, use the same input raster and the output .ecd file from this tool in the Classify Raster tool.
The input raster can be any Esri-supported raster with any valid bit depth.
To create a segmented raster dataset, use the Segment Mean Shift tool.
To create the training sample file, use the Training Samples Manager pane from the Classification Tools drop-down menu.
The Output Classifier Definition File contains attribute statistics suitable for the Maximum Likelihood Classification tool.
The Segment Attributes parameter is enabled only if one of the raster layer inputs is a segmented image.
A two step process is necessary to classify time series raster data using the Continuous Change Detection and Classification (CCDC) algorithm. First, run the Analyze Changes Using CCDC tool, which is available with an Image Analyst extension license. Next, use those results as input to this training tool.
The training sample data must have been collected at multiple times using the Training Samples Manager. The dimension value for each sample is listed in a field in the training sample feature class, which is specified in the Dimension Value Field parameter.

Syntax

TrainMaximumLikelihoodClassifier(in_raster, in_training_features, out_classifier_definition, {in_additional_raster}, {used_attributes}, {dimension_value_field})

Parameter	Explanation	Data Type
in_raster	The raster dataset to classify.	Raster Layer; Mosaic Layer; Image Service; String
in_training_features	The training sample file or layer that delineates the training sites. These can be either shapefiles or feature classes that contain your training samples. The following field names are required in the training sample file: classname—A text field indicating the name of the class category classvalue—A long integer field containing the integer value for each class category	Feature Layer
out_classifier_definition	The output JSON file that contains attribute information, statistics, hyperplane vectors, and other information for the classifier. An .ecd file is created.	File
in_additional_raster (Optional)	Incorporates ancillary raster datasets, such as a segmented image or DEM. This parameter is optional.	Raster Layer; Mosaic Layer; Image Service; String
used_attributes [used_attributes,...] (Optional)	Specifies 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) is derived from the optional pixel image on a per-segment basis. STD —The standard deviation is 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 to the tool is a segmented image, the default attributes are COLOR, COUNT, COMPACTNESS, and RECTANGULARITY. If an in_additional_raster is included as an input with a segmented image, MEAN and STD are also available attributes.	String
dimension_value_field (Optional)	Contains dimension values in the input training sample feature class. This parameter is required to classify a time series of raster data using the change analysis raster output from the Analyze Changes Using CCDC tool in the Image Analyst toolbox.	Field

Code sample

TrainMaximumLikelihoodClassifier example 1 (Python window)

The following Python window script demonstrates how to use the TrainMaximumLikelihoodClassifier tool.

import arcpy
from arcpy.sa import *

TrainMaximumLikelihoodClassifier(
    "c:/test/moncton_seg.tif", "c:/test/train.gdb/train_features", 
    "c:/output/moncton_sig.ecd", "c:/test/moncton.tif", 
    "COLOR;MEAN;STD;COUNT;COMPACTNESS;RECTANGULARITY")

TrainMaximumLikelihoodClassifier example 2 (stand-alone script)

This example shows how to train a maximum likelihood classifier.

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


# Set local variables
inSegRaster = "c:/test/moncton_seg.tif"
train_features = "c:/test/train.gdb/train_features"
out_definition = "c:/output/moncton_sig.ecd"
in_additional_raster = "c:/moncton.tif"
attributes = "COLOR;MEAN;STD;COUNT;COMPACTNESS;RECTANGULARITY"

# Execute 
TrainMaximumLikelihoodClassifier(inSegRaster, train_features, out_definition, 
                                 in_additional_raster, attributes)

TrainMaximumLikelihoodClassifier example 3 (stand-alone script)

This example shows how to train a maximum likelihood classifier using a change analysis raster from the Analyze Changes Using CCDC tool.

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

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


# Set local variables
in_changeAnalysisRaster = "c:/test/LandsatCCDC.crf"
train_features = "c:/test/train.gdb/train_features"
out_definition = "c:/output/change_detection.ecd"
additional_raster = ''
attributes = None
dimension_field = "DateTime"

# Execute
arcpy.sa.TrainMaximumLikelihoodClassifier(
	in_changeAnalysisRaster, train_features, out_definition,
	additional_raster, attributes, dimension_field)

Environments

Current Workspace, Extent, Geographic Transformations, Output Coordinate System, Scratch Workspace, Snap Raster

Licensing information

Basic: Requires Spatial Analyst or Image Analyst
Standard: Requires Spatial Analyst or Image Analyst
Advanced: Requires Spatial Analyst or Image Analyst