# Semi-supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification

Source: [Semi-supervised Learning with Graphs: Covariance Based Superpixels For Hyperspectral Image Classification](<https://www.sota2.com/research/paper/68fa65bc1e209131cea0c063>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:38.919Z

Authors: Philip Sellars, Angelica Aviles-Rivero, Nicolas Papadakis, David Coomes, Anita Faul, Carola-Bibane Sch\\"onlieb

Published: 2019-01-14T11:18:27.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1901.04240>)

[PDF](<https://arxiv.org/pdf/1901.04240.pdf>)