SELLARS, Philip; AVILES-RIVERO, Angelica I.; PAPADAKIS, Nicolas; COOMES, David; FAUL, Anita; SCHÖNLIEB, Carola-Bibiane

Metadatos

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Licencia de uso del documento

SELLARS, Philip

AVILES-RIVERO, Angelica I.
Department of Applied Mathematics and Theoretical Physics [DAMTP]

PAPADAKIS, Nicolas
Institut de Mathématiques de Bordeaux [IMB]

Idioma

Communication dans un congrès

Este ítem está publicado en

IEEE Geoscience and Remote Sensing Symposium (IGARSS'19), 2019-07-28, Yokohama. p. 592-595

Resumen en inglés

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.< Leer menos

Palabras clave en inglés

Index Terms-Hyperspectral Imaging

Superpixels

Covariance

Graphs

Semi-Supervised Learning

Classification

URI

https://oskar-bordeaux.fr/handle/20.500.12278/192808

Proyecto europeo

Nonlocal Methods for Arbitrary Data Sources

Orígen

Importado de Hal

Centros de investigación

Institut de Mathématiques de Bordeaux (IMB) - UMR 5251