We propose a method for the unsupervised clustering of hyperspectral images based on spatially regularized spectral with ultrametric path distances. The proposed efficiently combines data density and spectral-spatial geometry to distinguish between material classes in data, without need training labels. is efficient, quasilinear scaling number points, enjoys robust theoretical performance guara...