Takes the spectrum from x numbers of pixels to make representative spectrum. The pixels can be chosen in one out of five methods.



  • 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 binning

    • The pixels are distributed using PCA to get a spread containing a variety of spectrums for the objects.

  • Spectral space filling

    • 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 of the representative spectrum.


✅ Adds only unique spectrum

⬜ Can have the same spectrum from different representative spectrum pixels.


Size of the objects created from the segmentation

1x1 will create objects 1 pixel in size.


Only applicable for Spectral Clustering (k-means)

Specify number of clusters (k) to partition the observations into.