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Semi-supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification
| dc.contributor.author | SELLARS, Philip | |
| hal.structure.identifier | Department of Applied Mathematics and Theoretical Physics [DAMTP] | |
| dc.contributor.author | AVILES-RIVERO, Angelica I. | |
| hal.structure.identifier | Institut de Mathématiques de Bordeaux [IMB] | |
| dc.contributor.author | PAPADAKIS, Nicolas | |
| hal.structure.identifier | Plant Sciences, University of Cambridge | |
| dc.contributor.author | COOMES, David | |
| dc.contributor.author | FAUL, Anita | |
| hal.structure.identifier | Department of Applied Mathematics and Theoretical Physics [DAMTP] | |
| dc.contributor.author | SCHÖNLIEB, Carola-Bibiane | |
| dc.date.accessioned | 2024-04-04T03:00:19Z | |
| dc.date.available | 2024-04-04T03:00:19Z | |
| dc.date.conference | 2019-07-28 | |
| dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/192808 | |
| dc.description.abstractEn | 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. | |
| dc.language.iso | en | |
| dc.subject.en | Index Terms-Hyperspectral Imaging | |
| dc.subject.en | Superpixels | |
| dc.subject.en | Covariance | |
| dc.subject.en | Graphs | |
| dc.subject.en | Semi-Supervised Learning | |
| dc.subject.en | Classification | |
| dc.title.en | Semi-supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification | |
| dc.type | Communication dans un congrès | |
| dc.subject.hal | Informatique [cs]/Traitement du signal et de l'image | |
| dc.identifier.arxiv | 1901.04240 | |
| dc.description.sponsorshipEurope | Nonlocal Methods for Arbitrary Data Sources | |
| bordeaux.page | 592-595 | |
| bordeaux.hal.laboratories | Institut de Mathématiques de Bordeaux (IMB) - UMR 5251 | * |
| bordeaux.institution | Université de Bordeaux | |
| bordeaux.institution | Bordeaux INP | |
| bordeaux.institution | CNRS | |
| bordeaux.conference.title | IEEE Geoscience and Remote Sensing Symposium (IGARSS'19) | |
| bordeaux.country | JP | |
| bordeaux.conference.city | Yokohama | |
| bordeaux.peerReviewed | oui | |
| hal.identifier | hal-02057730 | |
| hal.version | 1 | |
| hal.invited | non | |
| hal.proceedings | oui | |
| hal.conference.end | 2019-08-02 | |
| hal.popular | non | |
| hal.audience | Internationale | |
| hal.origin.link | https://hal.archives-ouvertes.fr//hal-02057730v1 | |
| bordeaux.COinS | ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.spage=592-595&rft.epage=592-595&rft.au=SELLARS,%20Philip&AVILES-RIVERO,%20Angelica%20I.&PAPADAKIS,%20Nicolas&COOMES,%20David&FAUL,%20Anita&rft.genre=unknown |
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