Representative spectrum
Takes the spectrum from numbers of objects to make representative spectrum. The pixels can be chosen using a number of different algorithms, explained below.
Parameters
Method
Evenly Spread
The pixels are evenly spread across the images/objects.
Random
The pixels are randomly spread across the images/objects.
Random(Gaussian)
The pixels are randomly spread with a larger concentration of pixels in the center of the images/objects.
Spectral Evenly
The pixels are distributed using PCA score and spread evenly sorted by sum of squares for all components
Spectral Binning
The pixels are distributed using PCA to get a spread containing a variety of spectrum for the objects.
Spectral Space Filling (Very slow)
The pixels are distributed using PCA and iterated to find the spectrum containing the best variety to describe the spectrum of the objects.
Spectral Clustering (k-means)
The pixels are distributed using k-means clustering to partition the pixels into k number of clusters and select pixels for the partitioned clusters.
Number
Number of objects to create, .
Unique
✅ Adds only unique spectrum
⬜ Can have the same spectrum from different representative spectrum pixels.
Dimensions
Size of the objects created from the segmentation
1x1
will create objects 1 pixel in size.
Clusters
Only applicable for Spectral Clustering (k-means)
Specify number of clusters () to partition the observations into.
Applies to
Only visible when applicable
When Applies to is used only objects from the selected segmentation will be used for the next segmentation on the analyse tree.
This is denoted by the dashed line from the Object node to the segmentation which only is applied to a subset of all applicable segmentation.
Link
Only visible when applicable
Link output objects from two or more segmentations to top segmentation. Descriptors can then be added to the common object output and will be calculated for objects from all segmentations.
The segmentations must be at same level to be available for linking.