Train Maximum Likelihood Classifier (Spatial Analyst)

Available with Spatial 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.

• The Output Classifier Definition File contains attribute statistics suitable for the Maximum Likelihood Classification tool.

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

• 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.

Parameters

Dialog

Label	Explanation	Data Type
Input Raster	The raster dataset to classify.	Raster Layer; Mosaic Layer; Image Service; String
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. 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.	Field

Python

TrainMaximumLikelihoodClassifier(in_raster, 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
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. 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. 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.	String
dimension_value_field (Optional)	Contains dimension values in the input training sample feature class.	Field

Environments

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