  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. The M-SRDL
clustering algorithm extracts clusterings at many scales from a hyperspectral
image 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 results in
smoother and more coherent clusters when applied to hyperspectral data,
yielding more accurate clustering labels.
