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

2019-01-14 · Philip Sellars, Angelica Aviles-Rivero, Nicolas Papadakis, David Coomes, Anita Faul, Carola-Bibane Schönlieb

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.

📄 PDF Abstract BibTeX arXiv:1901.04240

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