Applicability of UAV-based optical imagery and classification algorithms for detecting pine wilt disease at different infection stages
Applicability of UAV-based optical imagery and classification algorithms for detecting pine wilt disease at different infection stages
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基于无人机的光学图像和分类算法在不同感染阶段检测松材线虫病的适用性
DOI：
10.1080/15481603.2023.2170479
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发表时间：
2023-01
影响因子：
6.7
通讯作者：
Tan Sun
中科院分区：
文献类型：
--
作者：
Ning Zhang;Xiujuan Chai;Niwen Li;Jianhua Zhang;Tan Sun
摘要作为气候变化背景下具有快速传播趋势的检疫性疾病，松树萎蔫病在不同侵染阶段的准确检测和定位对维持森林健康和高产具有重要意义。近年来，基于无人机的光学遥感影像为及时、准确地监测PWD提供了新的手段。针对基于无人机的图像分类，已经提出了许多相应的分析算法，但尚未在统一的条件和标准下对其检测不同PWD感染阶段的适用性进行评估。本研究旨在系统评估多源图像检测PWD不同感染阶段的性能，分析有效的分类算法，并进一步分析热图像对PWD早期检测的有效性。本研究采用基于无人机的高光谱（HSI）、多光谱（MSI）和带热波段的MSI （MSI& tir）数据集作为数据源，将PWD感染分为健康期、黄化期、红灰色期4个阶段。将光谱分析、支持向量机（SVM）、随机森林（RF）、二维和三维卷积网络（2D和3D-CNN）算法应用于这些数据集，比较它们的分类能力。结果表明：(1)MSI数据集对健康阶段、红色阶段和灰色阶段的分类精度与使用相同算法的MSI和tir数据集的分类精度接近，而HSI数据集没有明显的优势。（II） RF和3D-CNN算法在所有数据集上的准确率最高（RF：总体准确率为94.26%，3D-CNN：总体准确率为93.31%），光谱分析方法对MSI&TIR数据集也有效。(3)热带在黄化期检测中表现出显著的潜力，MSI&TIR数据集在所有感染期的检测中表现最好。考虑到这一点，我们认为MSI&TIR数据集基本上可以满足各个阶段的PWD识别需求，RF算法是最佳选择，特别是在实际森林调查中。此外，热成像在PWD早期监测中的表现值得进一步研究。这些发现有望为未来的研究和实际调查提供见解，以选择遥感数据集和数据分析算法，以满足不同PWD感染阶段的检测要求，从而更早地发现疾病并防止损失。
ABSTRACT As a quarantine disease with a rapid spread tendency in the context of climate change, accurate detection and location of pine wilt disease (PWD) at different infection stages is critical for maintaining forest health and being highly productivity. In recent years, unmanned aerial vehicle (UAV)-based optical remote-sensing images have provided new instruments for timely and accurate PWD monitoring. Numerous corresponding analysis algorithms have been proposed for UAV-based image classification, but their applicability of detecting different PWD infection stages has not yet been evaluated under a uniform conditions and criteria. This research aims to systematically assess the performance of multi-source images for detecting different PWD infection stages, analyze effective classification algorithms, and further analyze the validity of thermal images for early detection of PWD. In this study, PWD infection was divided into four stages: healthy, chlorosis, red and gray, and UAV-based hyperspectral (HSI), multispectral (MSI), and MSI with a thermal band (MSI&TIR) datasets were used as the data sources. Spectral analysis, support vector machine (SVM), random forest (RF), two- and three-dimensional convolutional network (2D- and 3D-CNN) algorithms were applied to these datasets to compare their classification abilities. The results were as follows: (I) The classification accuracy of the healthy, red, and gray stages using the MSI dataset was close to that obtained when using the MSI&TIR dataset with the same algorithms, whereas the HSI dataset displayed no obvious advantages. (II) The RF and 3D-CNN algorithms were the most accurate for all datasets (RF: overall accuracy = 94.26%, 3D-CNN: overall accuracy = 93.31%), while the spectral analysis method is also valid for the MSI&TIR dataset. (III) Thermal band displayed significant potential in detection of the chlorosis stage, and the MSI&TIR dataset displayed the best performance for detection of all infection stages. Considering this, we suggest that the MSI&TIR dataset can essentially satisfy PWD identification requirements at various stages, and the RF algorithm provides the best choice, especially in actual forest investigations. In addition, the performance of thermal imaging in the early monitoring of PWD is worthy of further investigation. These findings are expected to provide insight into future research and actual surveys regarding the selection of both remote sensing datasets and data analysis algorithms for detection requirements of different PWD infection stages to detect the disease earlier and prevent losses.
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DOI：
10.14578/jkfs.2013.102.4.499
发表时间：
2013-12
期刊：
Journal of the Korean Forestry Society
影响因子：
--
作者：
김소라;이우균;남기준;송용호;유항남;김문일;이종열;이승호
通讯作者：
김소라;이우균;남기준;송용호;유항남;김문일;이종열;이승호
使用基于无人机的高光谱图像评估油松毛虫 Tsai 和 Liu 灾害爆发期间的落叶情况
影响因子：
13.5
作者：
Zhang Ning;Zhang Xiaoli;Yang Guijun;Zhu Chenghao;Huo Langning;Feng Haikuan
通讯作者：
Feng Haikuan
使用无人机图像和深度学习算法识别松材线虫病
影响因子：
5
作者：
Qin Jun;Wang Biao;Wu Yanlan;Lu Qi;Zhu Haochen
通讯作者：
Zhu Haochen
近三十年来高光谱植物病害检测先进技术与发展回顾
影响因子：
5
作者：
Ning Zhang;Guijun Yang;Yuchun Pan;Xiaodong Yang;Liping Chen;Chunjiang Zhao
通讯作者：
Chunjiang Zhao
影响因子：
9.5
作者：
C. Stone;C. Mohammed
通讯作者：
C. Stone;C. Mohammed