  Clustering algorithms partition a dataset into groups of similar points. The
primary contribution of this article is the Multiscale Spatially-Regularized
Diffusion Learning (M-SRDL) clustering algorithm, which uses
spatially-regularized diffusion distances to efficiently and accurately learn
multiple scales of latent structure in hyperspectral images (HSI). The M-SRDL
clustering algorithm extracts clusterings at many scales from an HSI and
outputs these clusterings' variation of information-barycenter as an exemplar
for all underlying cluster structure. We show that incorporating spatial
regularization into a multiscale clustering framework corresponds to smoother
and more coherent clusters when applied to HSI data and leads to more accurate
clustering labels.
