Available with Spatial Analyst license.
Summary
Identify features or segments in your imagery by grouping adjacent pixels together that have similar spectral characteristics. You may control the amount of spatial and spectral smoothing to help derive features of interest.
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.
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})| Parameter | Explanation | Data Type |
in_raster | Select the raster dataset you want to segment. This can be a multispectral or grayscale image. To achieve optimal results, use the Symbology tab in the dataset properties to interactively stretch your imagery 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. | Raster Dataset | Mosaic Dataset | Raster Layer | Mosaic Layer | Image Service 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 one 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. Units are in pixels. | Long |
band_indexes (Optional) | Select the bands you want to use to segment your imagery separated by a space. You want to select bands that offer the most differentiation between the features of interest. | String |
Return Value
| Name | Explanation | Data 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 segment mean shift 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"
# Check out the ArcGIS Spatial Analyst extension license
arcpy.CheckOutExtension("Spatial")
# 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")
Environments
Licensing Information
- ArcGIS for Desktop Basic: Requires Spatial Analyst
- ArcGIS for Desktop Standard: Requires Spatial Analyst
- ArcGIS for Desktop Advanced: Requires Spatial Analyst