ArXiv Preprint
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.
Sam L. Polk, Aland H. Y. Chan, Kangning Cui, Robert J. Plemmons, David A. Coomes, James M. Murphy
2022-04-19
