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In this paper, we present a graph-based semi-supervised framework forhyperspectral image classification. We first introduce a novel superpixelalgorithm based on the spectral covariance matrix representation of pixels toprovide a better representation of our data. We then construct a superpixelgraph, based on carefully considered feature vectors, before performingclassification. We demonstrate, through a set of experimental results using twobenchmarking datasets, that our approach outperforms three state-of-the-artclassification frameworks, especially when an extremely small amount oflabelled data is used.

Semi-supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification