Available with Spatial Analyst license.
Identify features or segments in your imagery by grouping adjacent pixels together that have similar spectral characteristics. You can control the amount of spatial and spectral smoothing to help derive features of interest.
There are five inputs for this function:
- Input Raster
- Spectral Detail
- Spatial Detail
- Min Segment Size in Pixels
- Segment boundaries only
The spectral detail sets the level of importance given to the spectral differences of features in your imagery.
Valid floating-point 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, using a higher spectral detail in a forested scene allows you to better distinguish the different tree species.
The spatial detail sets the level of importance given to the proximity between features in your imagery.
Valid integer 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.
Min Segment Size in Pixels
The minimum segment size, which is measured in pixels, will identify blocks of pixels that are considered too small to be considered a fragment. All segments that are smaller than the specified value will merge the smaller segments with their best fitting neighbor segment.
Segment boundaries only
The segment boundaries draws a black contour line around each segment. This is helpful so you can distinguish adjacent segments that have similar colors.
- 0—The segment boundaries are not displayed. This is the default.
- 1—The segment boundaries are displayed with black contour lines around each segment.