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

Abstract

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.

HSIS-Net: Hyperspectral Image Segmentation Using Multi-view Active Learning Based FCSN

“…16 dimension. The main difference between normal images and HSI is in the usage of the sampling technique, the bandwidth of each spectral channel, and the number of bands [11]. Multispectral remote sensing collects MSI data that have several bands each sampled at discrete, often discontinuous, wavelengths with wider spectral bandwidths, i.e., low spectral resolution.…”