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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 an extremely small amount of labelled data is used.
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https://hal.archives-ouvertes.fr/hal-02057730
Contributor : Nicolas Papadakis <>
Submitted on : Monday, July 22, 2019 - 5:48:27 PM
Last modification on : Monday, October 28, 2019 - 2:58:03 PM

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  • HAL Id : hal-02057730, version 1
  • ARXIV : 1901.04240

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Philip Sellars, Angelica I. Aviles-Rivero, Nicolas Papadakis, David Coomes, Anita Faul, et al.. Semi-supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification. IEEE Geoscience and Remote Sensing Symposium, Jul 2019, Yokohama, Japan. ⟨hal-02057730⟩

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