Compute Segment Attributes (Spatial Analyst)

Available with Spatial Analyst license.

Available with Image Analyst license.

Summary

Computes a set of attributes associated with the segmented image. The input raster can be a single-band or 3-band, 8-bit segmented image.

Usage

• This tool generates the attributes for each segment that exists in the image. Attributes include mean, standard deviation, segment size, converged color (from the Segment Mean Shift tool), and compactness.

• 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 Segmented RGB Or Gray Raster	The input segmented raster dataset, where all the pixels belonging to a segment have the same converged RGB color. Usually, it is an 8-bit, 3-band RGB raster, but it can also be a 1-band grayscale raster.	Raster Layer; Mosaic Layer
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.	Raster Layer; Mosaic Layer
Segment Attributes Used (Optional)	Specifies the attributes that will be included in the attribute table associated with the output raster. If the only input into the tool is a segmented image, the default attributes are Average chromaticity color, Count of pixels, Compactness, and Rectangularity. If an Additional Input Raster is also included as an input along with a segmented image, then Mean digital number and Standard deviation are available as options. Converged color—The RGB color values will be derived from the input raster on a per-segment basis. 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

Return Value

Label	Explanation	Data Type
Output Segment Index Raster	The output segment index raster, where the attributes for each segment are recorded in the associated attribute table.	Raster

Python

ComputeSegmentAttributes(in_segmented_raster, {in_additional_raster}, {used_attributes})

Name	Explanation	Data Type
in_segmented_raster	The input segmented raster dataset, where all the pixels belonging to a segment have the same converged RGB color. Usually, it is an 8-bit, 3-band RGB raster, but it can also be a 1-band grayscale raster.	Raster Layer; Mosaic Layer
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.	Raster Layer; Mosaic Layer
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. 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. 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

Return Value

Name	Explanation	Data Type
out_index_raster_dataset	The output segment index raster, where the attributes for each segment are recorded in the associated attribute table.	Raster

Code sample

ComputeSegmentAttributes example 1 (Python window)

This example computes segment attributes for a TIFF raster.

import arcpy
from arcpy.sa import *

compute_att = ComputeSegmentAttributes(
                  "c:/test/moncton_seg.tif", "c:/test/moncton.tif", 
                  "COLOR;MEAN;STD;COUNT;COMPACTNESS;RECTANGULARITY")

compute_att.save("c:/test/moncton_computeseg.tif")

ComputeSegmentAttributes example 2 (stand-alone script)

This example computes segment attributes for a TIFF raster.

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


# Set local variables
inSegRaster = "c:/test/moncton_seg.tif"
in_additional_raster = "c:/test/moncton.tif"
attributes = "COLOR;MEAN;STD;COUNT;COMPACTNESS;RECTANGULARITY"

# Execute 
compute_att = ComputeSegmentAttributes(inSegRaster, in_additional_raster, 
                                       attributes)
#save output 
compute_att.save("c:/test/moncton_computeseg.tif")

Environments

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

Licensing information

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