Evaluation of UAV imagery for mapping <i>Silybum marianum</i> weed patches

作     者：Tamouridou, A. A. Alexandridis, T. K. Pantazi, X. E. Lagopodi, A. L. Kashefi, J. Moshou, D. 

作者机构：Aristotle Univ Thessaloniki Fac Agr Lab Remote Sensing Univ CampusUniv Box 259 Thessaloniki 54124 Greece Aristotle Univ Thessaloniki Fac Agr GIS Univ CampusUniv Box 259 Thessaloniki 54124 Greece Aristotle Univ Thessaloniki Fac Agr Agr Engn Lab Thessaloniki Greece Aristotle Univ Thessaloniki Fac Agr Plant Pathol Lab Thessaloniki Greece ARS USDA European Biol Control Lab Thessaloniki Greece 

出 版 物：《INTERNATIONAL JOURNAL OF REMOTE SENSING》 (国际遥感杂志)

年 卷 期：2017年第38卷第8-10期

页      面：2246-2259页

核心收录：

学科分类：083002[工学-环境工程] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 1002[医学-临床医学] 08[工学] 09[农学] 0804[工学-仪器科学与技术] 0903[农学-农业资源与环境] 0816[工学-测绘科学与技术] 081602[工学-摄影测量与遥感] 081102[工学-检测技术与自动化装置] 0811[工学-控制科学与工程] 

基　　金：American Farm School of Thessaloniki 

主　　题：multispectral imagery Computer Programming Languages drone honey bees Weeds Unmanned Aerial Vehicle Patches images fixed wings Carduus marianus image resolution spatial distribution High resolution imaging 

摘      要：Silybum marianum (L.) Gaertn weed has the tendency to grow in patches. In order to perform site-specific weed management, determining the spatial distribution of weeds is important for its eradication. Remote sensing has been used to perform species discrimination and it offers promising techniques for operational weed mapping. In the present study, the feasibility of high-resolution imaging for S. marianum detection and mapping is reported. A multispectral camera (green-red-near-infrared) mounted on a fixed wing unmanned aerial vehicle (UAV) was used for the acquisition of high-resolution images with pixel size of 0.1 m. The maximum likelihood (ML) classifier was used to classify the S. marianum among other weed species present in a field, with Avena sterilisL. being predominant. As input to the classifier, the three spectral bands and the texture were used. The scale of the mapping was varied by degrading the image resolution to evaluate classification performance, with 1 m resolution providing the highest classification accuracy. The classification rates obtained using ML reached an overall accuracy of 87.04% with a K-hat statistic of 74%. The results prove the feasibility of operational S. marianum mapping using UAV and multispectral imaging.