结合三维密集点云的无人机影像大豆覆盖度提取  

Extraction of soybean coverage from UAV images combined with 3D dense point cloud

作　　者：何海清[1] 严椰丽 凌梦云 杨勤锐 陈婷 李麟 He Haiqing;Yan Yeli;Ling Mengyun;Yang Qinrui;Chen Ting;Li Lin(School of Surveying and Mapping Engineering,East China University of Technology,Nanchang 330013,China;Ningbo Alatu Digital Technology Co.,Ltd.,Ningbo 315000,China;The Second Monitoringand Application Center,China Earthquake Administration,Xi'an 710054,China;School of WaterResources&Environmental Engineering,East China University of Technology,Nanchang 330013,China;Jiangxi Yufeng Intelligent Agricultural Technology Co.,Ltd.,Ganzhou 341309,China)

机构地区：[1]东华理工大学测绘工程学院,南昌330013 [2]宁波市阿拉图数字科技有限公司,宁波315000 [3]中国地震局第二监测中心,西安710054 [4]东华理工大学水资源与环境工程学院,南昌330013 [5]江西裕丰智能农业科技有限公司,赣州341309

出　　处：《农业工程学报》2022年第2期201-209,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(41861062);抚州市青年科技领军人才计划项目(2020ED65);江西省水利厅科技项目(202123TGKT12);江西省03专项及5G项目(20204ABC03A04)。

摘　　要：针对仅依赖二维遥感影像提取大豆覆盖度难以剔除杂草等复杂背景干扰的问题,该研究提出一种结合三维密集点云的大豆覆盖度提取方法,利用改进的运动恢复结构(Structure from Motion,SfM)算法与半全局匹配(Semi-Global Matching,SGM)算法从无人机立体影像中生成高精度稠密的大豆叶面真彩色三维点云,通过伽马增强的可见光绿叶指数提取植被信息,采用最佳结构元的局部阈值分割算法消除低矮杂草等噪声干扰,以达到结合可见光谱与三维点云实现复杂背景下大豆覆盖度提取的目的。选取不同时期、不同杂草混杂程度、不同地形起伏背景的大豆种植区无人机可见光影像进行试验。结果表明,该方法适用于复杂背景下的花芽分化期大豆覆盖度提取,伽马增强的绿叶指数可提高植被提取精度,结合三维点云信息的覆盖度提取总体精度达到98%以上,相比支持向量机、结合Lab颜色空间变换与Kmeans分割法、双峰阈值法等常用方法效率提高至少68%,在精度和效率方面明显优于仅利用二维影像的覆盖度提取方法。研究成果对于农田精细化管理和产量估测等具有重要的参考价值。An accurate extraction of soybean coverage cannot be gained only by using two-dimensional(2D)remote sensing images with complex background interference,such as weeds.In this study,a novel extraction was proposed for the soybean coverage under a complex background,combining the visible light spectrum and three-dimensional(3D)dense point clouds.The improved Structure from Motion(Sf M)and Semi-Global Matching(SGM)were used to extract the high-precision and dense RGB 3D point clouds from the Unmanned Aerial Vehicle(UAV)overlapping images.A gamma-enhanced visible light vegetation index was explored to extract the vegetation information.A local threshold segmentation was selected with the best structural element to eliminate the low weeds and other noises of non-soybean crops.A field experiment was carried out using the visible light UAV data of soybean planting areas under the different periods,the weed mixing,and topographic fluctuation.The 3D dense point cloud was then selected to generate the irregular triangular network and digital surface grid in the two study areas,where the height difference between crops and background objects was mapped into the gray difference of the grid map.The local threshold function was obtained through the top-hat transform and Otsu method,where the digital surface grid was binarized to accurately separate the crop from the background under the uneven gray distribution.Furthermore,the 3D dense point cloud and intersection analysis were combined to filter the low weeds and non-soybean vegetation.The crop and background samples were manually marked in the orthophoto that derived from UAV photogrammetry.As such,the confusion matrix was constructed to evaluate the classification accuracy of the extraction.It was found that the overall classification accuracies(OA)of soybean coverage extraction were 99.30%and 98.75%in study areas 1 and 2,respectively,where the Kappa coefficients were 0.99 and 0.98,respectively.A comparative analysis was also made to verify the effectiveness and applicabilit

关 键 词：无人机 作物 三维点云 覆盖度 伽马增强 植被指数 局部阈值分割 

分 类 号：S127[农业科学—农业基础科学]