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Segment Mean Shift

Available with Image Analyst license.

Available with Spatial Analyst license.

Summary

Groups together adjacent pixels that have similar spectral characteristics into segments.

Usage

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

  • The Band Index parameter is a list of three bands, separated by a space delimiter.

  • To achieve optimal results, use the Symbology tab in the dataset properties to interactively stretch your Input Raster so the features you want to classify are apparent. Then use these optimal settings in the Stretch raster function to enhance your imagery for optimum results, and specify the Output Pixel Type as 8 bit unsigned from the General tab.

    The output layer from the previously executed Stretch raster function can be the Input Raster for the Segment Mean Shift tool.

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

Syntax

SegmentMeanShift (in_raster, {spectral_detail}, {spatial_detail}, {min_segment_size}, {band_indexes})
ParameterExplanationData Type
in_raster

Select the raster dataset you want to segment. This can be a multispectral or grayscale image.

Raster Layer; Mosaic Layer
spectral_detail
(Optional)

Set the level of importance given to the spectral differences of features in your imagery.

Valid values range from 1.0 to 20.0. A higher value is appropriate when you have features you want to classify separately but have somewhat similar spectral characteristics. Smaller values create spectrally smoother outputs. For example, with higher spectral detail in a forested scene, you will be able to have greater discrimination between the different tree species.

Double
spatial_detail
(Optional)

Set the level of importance given to the proximity between features in your imagery.

Valid values range from 1.0 to 20. A higher value is appropriate for a scene where your features of interest are small and clustered together. Smaller values create spatially smoother outputs. For example, in an urban scene, you could classify an impervious surface using a smaller spatial detail, or you could classify buildings and roads as separate classes using a higher spatial detail.

Long
min_segment_size
(Optional)

Merge segments smaller than this size with their best fitting neighbor segment. This is related to the minimum mapping unit for your project.

Units are in pixels.

Long
band_indexes
(Optional)

Select the bands you want to use to segment your imagery, separated by a space. If no band indexes are specified, they are chosen by the following criteria:

  • If the raster has only 3 bands, those 3 bands are used
  • If the raster has more than 3 bands, the tool assigns the red, green and blue bands according to the raster's properties.
  • If the red, green and blue bands are not identified in the raster dataset's properties, bands 1, 2, and 3 are used.

The band order will not change the result.

You want to select bands that offer the most differentiation between the features of interest.

String

Return Value

NameExplanationData Type
out_raster_dataset

Specify a name and extension for the output dataset.

If your input was a multispectral image, the output will be an 8-bit RGB image. If the input was a grayscale image, the output will be an 8-bit grayscale image.

Raster

Code sample

SegmentMeanShift example 1 (Python window)

This example creates an output with a minimum segment size of 20, using the near-infrared, red, and green inputs.

import arcpy
from arcpy.sa import *

seg_raster = SegmentMeanShift("c:/test/moncton.tif", "15", "10", "20", "4 3 2")

seg_raster.save("c:/test/moncton_seg.tif")
SegmentMeanShift example 2 (stand-alone script)

This example performs a SegmentMeanShift to create an output with a minimum segment size of 20, using the near-infrared, red, and green inputs.

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


# Set local variables
inRaster = "c:/test/moncton.tif"
spectral_detail = "14.5"
spatial_detail = "10"
min_segment_size = "20"
band_indexes = "4 3 2"

# Execute 
seg_raster = SegmentMeanShift(inRaster, spectral_detail, spatial_detail, 
                              min_segment_size, min_segment_size)

# Save the output 
seg_raster.save("c:/output/moncton_seg.tif")

Licensing information

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

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