Available with Image Analyst license.
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 with the source image visible underneath.
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.
The preview is based on raster functions that process pixels currently on display and resampled to display resolution. This may cause a slight difference between the preview and the actual persisted result for regional operations.
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 result in more smoothing and longer processing times. For example, a higher spectral detail value in a forested scene will result in 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 impervious surface features using a smaller spatial detail value, or you could classify buildings and roads as separate classes using a higher spatial detail value.
Minimum Segment Size
This parameter is directly related to your minimum mapping unit. Segments smaller than this size are merged with their best fitting neighbor segment.
Units are in pixels.