# Unsupervised detection of ash dieback disease (Hymenoscyphus fraxineus) using diffusion-based hyperspectral image clustering

arXiv: https://arxiv.org/abs/2204.09041 · HTML: https://mlsanity.com/paper/2204.09041/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 21 references that are in the map.
- First posted on arXiv 19 Apr 2022.

## Builds on (most cited first, 7 of 7)

- [Learning by Unsupervised Nonlinear Diffusion](https://mlsanity.com/paper/1810.06702/) (2018)
- [Spectral-Spatial Diffusion Geometry for Hyperspectral Image Clustering](https://mlsanity.com/paper/1902.05402/) (2019)
- [Learning by Active Nonlinear Diffusion](https://mlsanity.com/paper/1905.12989/) (2019)
- [Spatially regularized active diffusion learning for high-dimensional images](https://mlsanity.com/paper/1911.02155/) (2019)
- [A Multiscale Environment for Learning by Diffusion](https://mlsanity.com/paper/2102.00500/) (2021)
- [Multiscale Clustering of Hyperspectral Images Through Spectral-Spatial Diffusion Geometry](https://mlsanity.com/paper/2103.15783/) (2021)
- [Active Diffusion and VCA-Assisted Image Segmentation of Hyperspectral Images](https://mlsanity.com/paper/2204.06298/) (2022)

## Built on by (most cited first, 3 of 3)

- [Superpixel-based and Spatially-regularized Diffusion Learning for Unsupervised Hyperspectral Image Clustering](https://mlsanity.com/paper/2312.15447/) (2023)
- [A 3-stage Spectral-spatial Method for Hyperspectral Image Classification](https://mlsanity.com/paper/2204.09294/) (2022)
- [Deep Spatially-Regularized and Superpixel-Based Diffusion Learning for Unsupervised Hyperspectral Image Clustering](https://mlsanity.com/paper/2604.13307/) (2026)

## Abstract (arXiv)

Ash dieback (Hymenoscyphus fraxineus) is an introduced fungal disease that is causing the widespread death of ash trees across Europe. Remote sensing hyperspectral images encode rich structure that has been exploited for the detection of dieback disease in ash trees using supervised machine learning techniques. However, to understand the state of forest health at landscape-scale, accurate unsupervised approaches are needed. This article investigates the use of the unsupervised Diffusion and VCA-Assisted Image Segmentation (D-VIS) clustering algorithm for the detection of ash dieback disease in a forest site near Cambridge, United Kingdom. The unsupervised clustering presented in this work has high overlap with the supervised classification of previous work on this scene (overall accuracy = 71%). Thus, unsupervised learning may be used for the remote detection of ash dieback disease without the need for expert labeling.