Train Maximum Likelihood Classifier (Image Analyst Tools)

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, which is available with an Spatial Analyst extension license.
The Segment Attributes parameter is only active if one of the raster layer inputs is a segmented image.
To classify time series raster data using the Continuous Change Detection and Classification (CCDC) algorithm, first run the Analyze Changes Using CCDC tool. Then use the output change analysis raster as the input raster for 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.

Label	Explanation	Data type
Input Raster	The raster dataset to classify.	Raster Layer; Mosaic Layer; Image Service; String
Input Training Sample File	The training sample file or layer that delineates the training sites. These can be either shapefiles or feature classes that contain the 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
Output Classifier Definition File	The output JSON format file that will contain attribute information, statistics, hyperplane vectors, and other information for the classifier. An `.ecd` file will be created.	File
Additional Input Raster (Optional)	Incorporates ancillary raster datasets, such as a segmented image or DEM. This parameter is optional.	Raster Layer; Mosaic Layer; Image Service; String
Segment Attributes Used (Optional)	Specifies the attributes that will be included in the attribute table associated with the output raster. This parameter is only active 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 Converged color, Count of pixels, Compactness, and Rectangularity. If an Additional Input Raster value is included as an input with a segmented image, Mean digital number and Standard deviation are also available attributes. Converged color—The RGB color values will be derived from the input raster on a per-segment basis. This is also known as average chromaticity color. Mean digital number—The average digital number (DN) will be derived from the optional pixel image on a per-segment basis. Standard deviation—The standard deviation will be derived from the optional pixel image on a per-segment basis. Count of pixels—The number of pixels composing 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, in which 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, in which 1 is a rectangle.	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.	Field

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

Name	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 the 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 format file that will contain attribute information, statistics, hyperplane vectors, and other information for the classifier. An `.ecd` file will be 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 that will be included in the attribute table associated with the output raster. 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` value is included as an input with a segmented image, `MEAN` and `STD` are also available attributes. `COLOR`—The RGB color values will be derived from the input raster on a per-segment basis. This is also known as average chromaticity color. `MEAN`—The average digital number (DN) will be derived from the optional pixel image on a per-segment basis. `STD`—The standard deviation will be derived from the optional pixel image on a per-segment basis. `COUNT`—The number of pixels composing 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, in which 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, in which 1 is a rectangle.	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.	Field

Code sample

TrainMaximumLikelihoodClassifier example 1 (Python window)

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

import arcpy
from arcpy.ia 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.ia import *

"""
Usage: TrainMaximumLikelihoodClassifier(in_raster, in_training_features, out_classifier_definition,
                                       {in_additional_raster}, {used_attributes})
"""

# 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"

# Run
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.ia import *

# Define input parameters
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"

# Run
arcpy.ia.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