Semi-Supervised Learning with Graphs: Covariance Based Superpixels for Hyperspectral Image Classification
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Publication Date
2019Journal Title
International Geoscience and Remote Sensing Symposium (IGARSS)
Conference Name
IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium
ISSN
2153-6996
ISBN
9781538691540
Publisher
IEEE
Pages
592-595
Type
Conference Object
This Version
AM
Metadata
Show full item recordCitation
Sellars, P., Aviles-Rivero, A., Papadakis, N., Coomes, D., Faul, A., & Schonlieb, C. (2019). Semi-Supervised Learning with Graphs: Covariance Based Superpixels for Hyperspectral Image Classification. International Geoscience and Remote Sensing Symposium (IGARSS), 592-595. https://doi.org/10.1109/IGARSS.2019.8898189
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.
Sponsorship
Case Studentship with the NPL
Funder references
Engineering and Physical Sciences Research Council (EP/N014588/1)
Identifiers
External DOI: https://doi.org/10.1109/IGARSS.2019.8898189
This record's URL: https://www.repository.cam.ac.uk/handle/1810/293841
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Licence:
http://www.rioxx.net/licenses/all-rights-reserved
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