论文标题
使用基于扩散的高光谱图像聚类的无监督检测（hymenoscyphus fraxineus）
Unsupervised detection of ash dieback disease (Hymenoscyphus fraxineus) using diffusion-based hyperspectral image clustering
论文作者
论文摘要
Ash Dieback（Hymenoscyphus fraxineus）是一种引入的真菌疾病，正在欧洲造成灰树的广泛死亡。遥感高光谱图像编码已利用有监督的机器学习技术在灰树中检测死亡疾病的丰富结构。但是，要了解景观规模的森林健康状况，需要进行准确的无监督方法。本文调查了在英国剑桥附近的森林场所使用无监督的扩散和VCA辅助图像分割（D-VIS）聚类算法来检测Ash Dieback病。这项工作中介绍的无监督聚类与该场景先前工作的监督分类具有很高的重叠（总体准确性= 71％）。因此，无需进行无监督的学习可以用于远程检测灰度疾病，而无需专家标签。
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.
