Semi-supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification
Language
en
Communication dans un congrès
This item was published in
IEEE Geoscience and Remote Sensing Symposium (IGARSS'19), 2019-07-28, Yokohama. p. 592-595
English 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 ...Read more >
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.Read less <
English Keywords
Index Terms-Hyperspectral Imaging
Superpixels
Covariance
Graphs
Semi-Supervised Learning
Classification
European Project
Nonlocal Methods for Arbitrary Data Sources
Origin
Hal imported