Disponible con licencia de Image Analyst.
Disponible con una licencia de Spatial Analyst.
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.
Nota:
La vista previa se basa en funciones de ráster que procesan los píxeles que se muestran actualmente en la pantalla y se remuestrean para la resolución de visualización. Esto puede provocar una ligera diferencia entre la vista previa y el resultado persistente real de las operaciones regionales.
There are three parameters that control how your imagery is segmented.
Spectral Detail
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.
Spatial Detail
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.