Semi-Supervised Learning with Graphs: Covariance Based Superpixels for Hyperspectral Image Classification
International Geoscience and Remote Sensing Symposium (IGARSS)
MetadataShow full item record
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
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.
Case Studentship with the NPL
External DOI: https://doi.org/10.1109/IGARSS.2019.8898189
This record's URL: https://www.repository.cam.ac.uk/handle/1810/293841