  In this paper, we present a graph-based semi-supervised framework for
hyperspectral image classification. We first introduce a novel superpixel
algorithm based on the spectral covariance matrix representation of pixels to
provide a better representation of our data. We then construct a superpixel
graph, based on carefully considered feature vectors, before performing
classification. We demonstrate, through a set of experimental results using two
benchmarking datasets, that our approach outperforms three state-of-the-art
classification frameworks, especially when an extremely small amount of
labelled data is used.
