摘要
叶倾角是描述植被冠层结构的一种重要参数,叶倾角分布(leaf angle distribution,LAD)决定了植被冠层对辐射的截获量,也是遥感定量反演中的一个重要参数。目前实测叶倾角的方法费时、费力、主观性强、精度无法保证。提出了一种基于图像的玉米植株叶倾角概率密度函数提取方法,以求快速、精确、低成本地获取玉米植株叶倾角。首先,对图像提取骨架;然后,去除骨架图像中的毛刺、茎秆等信息,得到叶片骨架;最后,以2像素×20像素大小的搜索窗口搜索骨架提取出叶倾角。精度评价结果表明,乳熟期玉米叶倾角提取值与实测值的相关系数为0. 821 4,拔节期玉米叶倾角提取值与实测值相关系数为0. 908 7。结果表明该方法具有可行性,精度较高。
Leaf angle is an important parameter to describe the canopy structure of vegetation.The leaf angle distribution(LAD)determines the interception of vegetation canopy and is an important parameter in quantitative inversion of remote sensing.The current method of measuring the leaf angle is time-consuming,labor-intensive and subjective,with no accuracy guarantee.In this paper,image-based probability density function extraction for LAD of corn plant is proposed,which can extract LAD of corn plant quickly and accurately with low cost.Firstly,the skeleton is extracted from the image.Secondly,the information such as burrs and stems in the skeleton image is removed to obtain the leaf skeleton.Finally,the leaf angle is extracted by searching the skeleton with a search window of size 2×20.The results of precision evaluation show that the correlation coefficient between the measured value of the corn dip angle and the extracted value is 0.8214,and the correlation coefficient between measured and extracted values of the corn leaf angle at jointing stage is 0.9087,which suggests that the method is feasible and accurate with low cost.
作者
陈啸
边大红
崔彦宏
刘鑫莉
孟祥磊
苏伟
CHEN Xiao;BIAN Dahong;CUI Yanhong;LIU Xinli;MENG Xianglei;SU Wei(College of Land Science and Technology,China Agricultural University,Beijing 100083,China;Key Laboratory of Remote Sensing for Agri-Hazards,Ministry of Agriculture,Beijing 100083,China;College of Agronomy,Hebei Agricultural University,Baoding 071001,China;Institute of Remote Sensing and Geographic Information System,Peking University,Beijing 100871,China)
出处
《国土资源遥感》
CSCD
北大核心
2020年第1期75-80,共6页
Remote Sensing for Land & Resources
基金
十三五国家重点研发计划项目“黄淮海北部夏玉米超高产群个体发育规律与群体质量调控技术”(编号:2017YFD0300903)
国家自然科学基金项目“联合时序遥感影像和地基激光雷达的玉米生长过程监测方法研究”(编号:41671433)
中国农业大学2019年教师党支部书记“双带头人”科技创新培育专项“夏玉米封垄后生物量遥感反演方法研究”(编号:2019TC138)共同资助。
关键词
图像
玉米植株
叶倾角概率密度函数
骨架化
去毛刺
image
corn plant
leaf angle distribution function
skeletonization
deburring
