Available with Spatial Analyst license.
Segmenting an image
Segmentation is a key component of the object-based classification workflow. This process groups neighboring pixels together that are similar in color and have certain shape characteristics. In addition, you can use the Show Segmented Boundaries Only option if you want to display the segments as polygons.
After you run segmentation, you will want to see the underlying imagery to verify that the objects make sense. Press the L key to toggle on and off the transparency of the segmented image. The preview is closest to the output result when you zoom in to source resolution and make sure the display is large enough.
There are three parameters that control how your imagery is segmented.
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.
Set the level of importance given to the proximity between features in your imagery.
Valid values range from 1 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.
Minimum Segment Size
Merge segments smaller than this size with their best fitting neighbor segment.
Units are in pixels.