基于无人机影像匹配点云数据的喀斯特峡谷区火龙果单株提取研究

doi:10.3969/j.issn.1004-1524.2020.06.17

浙江农业学报 ›› 2020, Vol. 32 ›› Issue (6): 1092-1102.DOI: 10.3969/j.issn.1004-1524.2020.06.17

基于无人机影像匹配点云数据的喀斯特峡谷区火龙果单株提取研究

尹林江^1,2, 周忠发^1,2,*, 黄登红^1,2, 尚梦佳^1,2

1.贵州师范大学喀斯特研究院/地理与环境科学学院,贵州贵阳 550001;
2.国家喀斯特石漠化防治工程技术研究中心,贵州贵阳 550001

收稿日期:2019-12-09 出版日期:2020-06-25 发布日期:2020-06-24
通讯作者: *周忠发,E-mail:fa6897@163.com
作者简介:尹林江(1993—),男,贵州德江人,硕士研究生,研究方向为无人机山地遥感、GIS开发与应用。E-mail:ylj8575@163.com
基金资助:
国家自然科学基金(41661088); 国家重点研发计划(2018YFB0505400); 贵州省高层次创新型人才培养计划“百”层次人才(黔科合平台人才〔2016〕5674)

Extraction of individual plant of pitaya in Karst Canyon Area based on point cloud data of UAV image matching

YIN Linjiang^1,2, ZHOU Zhongfa^1,2,*, HUANG Denghong^1,2, SHANG Mengjia^1,2

1. School of Karst Science/School of Geography & Environmental Science, Guizhou Normal University, Guiyang 550001, China;
2. State Engineering Technology Institute for Karst Desertification Control, Guiyang 550001, China

Received:2019-12-09 Online:2020-06-25 Published:2020-06-24

摘要/Abstract

摘要： 针对无人机可见光影像对背景与目标地物混淆时提取识别难的问题,利用四旋翼无人机采集喀斯特峡谷区的火龙果影像匹配点云数据,对原始点云数据进行去噪、滤波和归一化等处理,通过建立高精度的数字高程模型(DEM)、数字表面模型(DSM),进而建立高精度的冠层高度模型(CHM),并以目视解译的火龙果株数为参照,对火龙果株数进行识别提取验证。结果表明,运用无人机影像匹配点云数据,通过冠层高度模型在一定程度上可以消除植株下方杂草的影响;当样地内的基础设施或存在地物高度与火龙果冠层接近,导致误提,错提率最高为8.55%,漏提率最高为12.28%;在各样区中,运用种子点进行火龙果株数提取的精度均在92.38%以上;运用植被冠层进行火龙果株数提取的精度均在90.68%以上。由此表明,运用无人机影像匹配点云数据提取火龙果具有快速、简单有效、成本低、精度可靠的特点,适用于喀斯特山区作物株数的快速提取,可以与基于颜色指数的提取方法互为补充。

关键词: 无人机, 影像匹配点云, 冠层高度模型, 单株提取, 火龙果, 喀斯特峡谷区

Abstract: It is difficult for the visible image of unmanned aerial vehicle (UAV) to recognize some confused backgrounds and targets. To solve this problem, the paper collected the pitaya images matching point cloud data in the areas of Karst Valley through using the four-rotor drones. After processing the original point cloud data with desiccation, filtering, normalization and so on, through the establishment of the digital elevation model (DEM), digital surface model (DSM) with high accuracy, and then a high-precision canopy height model (CHM) was established. Furthermore, with the visual interpretational strains of pitaya as a reference, the definite numbers of pitaya was identified, extracted and verified. The results indicated that the influence of weeds under the plants could be eliminated to some extent, through using UAV images matching point cloud data and canopy Height model. If the height of infrastructure or ground object in the sample area was approach to that of the pitaya, it would lead to false extraction. The highest false extraction rate was 8.55%, and the highest missed extraction rate was 12.28%. Among all the sample areas, the precision of the number of pitaya plants which were extracted by the seed points was more than 92.38%; yet that of pitaya plants extracted by using the vegetation canopy was more than 90.68%. It indicated that the means of using UAV image matching point cloud data to extract the pitaya, had the characteristics of high speed, easy to deposit, low cost, and reliable accuracy, which was suitable for the rapid extraction of the number of crop in Karst mountain areas. It could mutually complement with the extraction method based on the color index.

Key words: unmanned aerial vehicle, image matching point cloud, canopy height model, single plant extraction, pitaya, Karst Canyon Area

中图分类号:

S252

尹林江, 周忠发, 黄登红, 尚梦佳. 基于无人机影像匹配点云数据的喀斯特峡谷区火龙果单株提取研究[J]. 浙江农业学报, 2020, 32(6): 1092-1102.

YIN Linjiang, ZHOU Zhongfa, HUANG Denghong, SHANG Mengjia. Extraction of individual plant of pitaya in Karst Canyon Area based on point cloud data of UAV image matching[J]. , 2020, 32(6): 1092-1102.

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基于无人机影像匹配点云数据的喀斯特峡谷区火龙果单株提取研究

Extraction of individual plant of pitaya in Karst Canyon Area based on point cloud data of UAV image matching

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