  An unsupervised learning algorithm to cluster hyperspectral image (HSI) data
is proposed that exploits spatially-regularized random walks. Markov diffusions
are defined on the space of HSI spectra with transitions constrained to near
spatial neighbors. The explicit incorporation of spatial regularity into the
diffusion construction leads to smoother random processes that are more adapted
for unsupervised machine learning than those based on spectra alone. The
regularized diffusion process is subsequently used to embed the
high-dimensional HSI into a lower dimensional space through diffusion
distances. Cluster modes are computed using density estimation and diffusion
distances, and all other points are labeled according to these modes. The
proposed method has low computational complexity and performs competitively
against state-of-the-art HSI clustering algorithms on real data. In particular,
the proposed spatial regularization confers an empirical advantage over
non-regularized methods.
