  In this paper, we design a hierarchical clustering algorithm for
high-resolution hyperspectral images. At the core of the algorithm, a new
rank-two nonnegative matrix factorizations (NMF) algorithm is used to split the
clusters, which is motivated by convex geometry concepts. The method starts
with a single cluster containing all pixels, and, at each step, (i) selects a
cluster in such a way that the error at the next step is minimized, and (ii)
splits the selected cluster into two disjoint clusters using rank-two NMF in
such a way that the clusters are well balanced and stable. The proposed method
can also be used as an endmember extraction algorithm in the presence of pure
pixels. The effectiveness of this approach is illustrated on several synthetic
and real-world hyperspectral images, and shown to outperform standard
clustering techniques such as k-means, spherical k-means and standard NMF.
