Classify Raster (Spatial Analyst)

Available with Spatial Analyst license.

Summary

Classifies a raster dataset based on an Esri classifier definition file (.ecd) and raster dataset inputs.

The .ecd file contains all the information needed to perform a specific type of Esri-supported classification. The inputs to this tool must match the inputs used to generate the required .ecd file.

The .ecd file can be generated from any of the classifier training tools, such as Train Random Trees Classifier or Train Support Vector Machine Classifier.

Usage

• The input raster can be any Esri-supported raster and any valid bit depth.

• The input .ecd file contains attribute statistics suitable for the appropriate classifier.

• See Analysis environments and Spatial Analyst for additional details on the geoprocessing environments that apply to this tool.

Parameters

Dialog

Label	Explanation	Data Type
Input Raster	The raster dataset to classify.	Mosaic Layer; Raster Layer; Image Service; String; Raster Dataset; Mosaic Dataset
Input Classifier Definition File	The input Esri classifier definition file (.ecd) containing the statistics for the chosen attributes for the classifier.	File
Additional Input Raster (Optional)	Ancillary raster datasets, such as a multispectral image or a DEM, will be incorporated to generate attributes and other required information for the classifier. This raster is necessary when calculating attributes such as mean or standard deviation. This parameter is optional.	Mosaic Layer; Raster Layer; Image Service; String; Raster Dataset; Mosaic Dataset

Return Value

Label	Explanation	Data Type
Output Classified Raster	The path and name of the classified image you are creating. The output classified raster is defined by the input raster dataset and .ecd file inputs.	Raster

Python

ClassifyRaster(in_raster, in_classifier_definition, {in_additional_raster})

Name	Explanation	Data Type
in_raster	The raster dataset to classify.	Mosaic Layer; Raster Layer; Image Service; String; Raster Dataset; Mosaic Dataset
in_classifier_definition	The input Esri classifier definition file (.ecd) containing the statistics for the chosen attributes for the classifier.	File
in_additional_raster (Optional)	Ancillary raster datasets, such as a multispectral image or a DEM, will be incorporated to generate attributes and other required information for the classifier. This raster is necessary when calculating attributes such as mean or standard deviation. This parameter is optional.	Mosaic Layer; Raster Layer; Image Service; String; Raster Dataset; Mosaic Dataset

Return Value

Name	Explanation	Data Type
out_raster_dataset	The path and name of the classified image you are creating. The output classified raster is defined by the input raster dataset and .ecd file inputs.	Raster

Code sample

ClassifyRaster example 1 (Python window)

This example classifies an input TIFF raster.

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

classifiedraster = ClassifyRaster("c:/classifydata/moncton_seg.tif", 
                                  "c:/classifydata/moncton_sig.ecd", 
                                  "c:/classifydata/moncton.tif")

classifiedraster.save("c:/test/moncton_classified.tif")

ClassifyRaster example 2 (stand-alone script)

This example classifies an input TIFF raster.

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


# Set local variables
insegras = "c:/classifydata/moncton_seg.tif"
indef_file = "c:/classifydata/moncton_sig.ecd"
in_additional_raster = "c:/classifydata/moncton.tif"


# Execute 
classifiedraster = ClassifyRaster(insegras, indef_file, in_additional_raster)

#save output
classifiedraster.save("c:/test/moncton_classified.tif")

Environments

Compression, Current Workspace, Extent, Geographic Transformations, NoData, Output CONFIG Keyword, Output Coordinate System, Parallel Processing Factor, Pyramid, Resampling Method, Raster Statistics, Scratch Workspace, Snap Raster, Tile Size