Available with Image Analyst license.
Available with Spatial Analyst license.
Summary
Groups into segments adjacent pixels that have similar spectral characteristics.
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}, {max_segment_size})| Parameter | Explanation | Data Type |
in_raster | The raster dataset to segment. This can be a multispectral or grayscale image. | Mosaic Layer; Raster Layer |
spectral_detail (Optional) | 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 to classify separately that have similar spectral characteristics. Smaller values create spectrally smoother outputs. For example, with higher spectral detail in a forested scene, you will have greater discrimination between the tree species. | Double |
spatial_detail (Optional) | 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 in which 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) | The minimum size of a segment. 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) | The bands that will be used to segment the imagery, separated by a space. If no band indexes are specified, they are determined by the following criteria:
The band order will not change the result. Select bands that offer the most differentiation between the features of interest. | String |
max_segment_size (Optional) | The maximum size of a segment. Segments that are larger than the specified size will be divided. Use this parameter to prevent artifacts in the output raster resulting from large segments. Units are in pixels. The default value is -1, meaning there is no limit on the segment size. | Long |
Return Value
| Name | Explanation | Data Type |
| out_raster_dataset | Specify a name and extension for the output dataset. If the 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
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")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")Environments
Licensing information
- Basic: Requires Spatial Analyst or Image Analyst
- Standard: Requires Spatial Analyst or Image Analyst
- Advanced: Requires Spatial Analyst or Image Analyst