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

## TL;DR

This paper introduces a graph-based semi-supervised classification method for hyperspectral images using a novel covariance-based superpixel algorithm, which improves accuracy with minimal labeled data.

## Contribution

The paper proposes a new superpixel algorithm based on spectral covariance matrices and integrates it into a graph-based semi-supervised framework for hyperspectral image classification.

## Key findings

- Outperforms three state-of-the-art methods on benchmark datasets.
- Achieves higher accuracy with very limited labeled data.
- Demonstrates robustness across different datasets.

## 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.