Classification method of cultivated land based on UAV visible light remote sensing
作者机构：College of EngineeringSouth China Agricultural UniversityGuangzhou 510642China National Center for International Collaboration Research on Precision Agricultural Aviation Pesticide Spraying TechnologyGuangzhou 510642China
出 版 物：《International Journal of Agricultural and Biological Engineering》 (国际农业与生物工程学报（英文）)
年 卷 期：2019年第12卷第3期
页 面：103-109页
核心收录：
学科分类：08[工学] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 081102[工学-检测技术与自动化装置]
基 金：We acknowledge that this research work was financially supported by the Leading Talents of Guangdong Province Program(Project No.2016LJ06G689) Educational Commission of Guangdong Province of China for Platform(Project No.2015KGJHZ007) Science and Technology Planning Project of Guangdong Province(Project No.2017B010117010) China Agriculture Research System(Project No.CARS-15-22)
主 题：UAV visible band remote sensing extraction of cultivated land area object oriented method
摘 要：The accurate acquisition of the grain crop planting area is a necessary condition for realizing precision *** remote sensing has the advantages of low cost use,simple operation,real-time acquisition of remote sensor images and high ground *** is difficult to separate cultivated land from other terrain by using only a single feature,making it necessary to extract cultivated land by combining various features and hierarchical *** this study,the UAV platform was used to collect visible light remote sensing images of farmland to monitor and extract the area information,shape information and position information of *** on the vegetation index,texture information and shape information in the visible light band,the object-oriented method was used to study the best scheme for extracting cultivated land *** repeated experiments,it has been determined that the segmentation scale 50 and the consolidation scale 90 are the most suitable segmentation *** crops and other features are separated by using the band information and texture *** overall accuracy of this method is 86.40%and the Kappa coefficient is *** experimental results show that the UAV visible light remote sensing data can be used to classify and extract cultivated land with high ***,there are some cases where the finely divided plots are misleading,so further optimization and improvement are needed.
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