  Hyperspectral image (HSI) classification is a topic of active research. One
of the main challenges of HSI classification is the lack of reliable labelled
samples. Various semi-supervised and unsupervised classification methods are
proposed to handle the low number of labelled samples. Chief among them are
graph convolution networks (GCN) and their variants. These approaches exploit
the graph structure for semi-supervised and unsupervised classification. While
several of these methods implicitly construct edge-weights, to our knowledge,
not much work has been done to estimate the edge-weights explicitly. In this
article, we estimate the edge-weights explicitly and use them for the
downstream classification tasks - both semi-supervised and unsupervised. The
proposed edge-weights are based on two key insights - (a) Ensembles reduce the
variance and (b) Classes in HSI datasets and feature similarity have only
one-sided implications. That is, while same classes would have similar
features, similar features do not necessarily imply the same classes.
Exploiting these, we estimate the edge-weights using an aggregate of ensembles
of watersheds over subsamples of features. These edge weights are evaluated for
both semi-supervised and unsupervised classification tasks. The evaluation for
semi-supervised tasks uses Random-Walk based approach. For the unsupervised
case, we use a simple filter using a graph convolution network (GCN). In both
these cases, the proposed edge weights outperform the traditional approaches to
compute edge-weights - Euclidean distances and cosine similarities.
Fascinatingly, with the proposed edge-weights, the simplest GCN obtained
results comparable to the recent state-of-the-art.
