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

ABSTRACT 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 a extremely small amount of labelled data is used. Index Terms-- Hyperspectral Imaging, Superpixels, Covariance, Graphs,Semi-Supervised Learning, Classification 1. INTRODUCTION Hyperspectral image (HSI) classification is an active area of research and poses unique challenges.