  We propose a method for the unsupervised clustering of hyperspectral images
based on spatially regularized spectral clustering with ultrametric path
distances. The proposed method efficiently combines data density and geometry
to distinguish between material classes in the data, without the need for
training labels. The proposed method is efficient, with quasilinear scaling in
the number of data points, and enjoys robust theoretical performance
guarantees. Extensive experiments on synthetic and real HSI data demonstrate
its strong performance compared to benchmark and state-of-the-art methods. In
particular, the proposed method achieves not only excellent labeling accuracy,
but also efficiently estimates the number of clusters.
