基于无人机遥感的喀斯特高原峡谷区火龙果单株识别提取方法

Recognition and Extraction Method of Single Dragon Fruit Plant in Plateau-Canyon Areas Based on UAV Remote Sensing

基于小型无人机搭载可见光镜头作为数据获取手段,通过对比可见光波段差异植被指数(VDVI)、过绿指数(ExG)、过绿减过红指数(ExG-ExR),归一化绿红差异指数NGRDI和绿叶指数(GLI)5种可见光波段颜色指数方法应用于喀斯特高原峡谷区典型经济作物火龙果植株识别的适宜性,结合可视化空间建模工具模型构建器,提出一种以单植株平均面积分割株丛的思想,对火龙果进行植株识别分割和单株提取方法研究,结果表明:1 ) VDVI相比EXG、NGRDI、ExG-ExR、GLI等植被指数更适宜作为火龙果的识别分割方法;2)当VDVI的OTSU阈值取0.037时,能最大程度地分割目标地物与背景地物;3 )通过人机交互基地实测获取的实际株数与提取株数验证,获得火龙果植株单株提取精度为91.7%。结果证实:以低空无人机可见光波段影像为数据源,VDVI指数作为识别方法,株丛分割应用于喀斯特高原峡谷区火龙果的单株识别提取具有可行性。

Based on a small-sized Unmanned Aerial Vehicle (UAV) with a visible light lens for data acquisition, and with the flight height set to 80 m, the heading overlap is 75% and the side overlap is 70% in obtaining centimeter level visible images. The five types of visible spectral-index based methods including Visible-Band-Difterence- Vegetation-Index (VDVI), Excess-Green index (ExG), Excess-Green minus Excess-Red index (ExG-ExR), Normalized Green-Red Difference Index (NGRDI), and the Green Leaf Index (GLI) were applied to distinguish pitaya fruit of typical economic crops in karst plateau gorge areas. The results showed that VDVI, ExG, and ExG- ExR are effective in separating target features from the soil, gravel, ridge, and subtilis, while GLI and NGRDI completely intersect, and the overlap phenomenon is noticeable with the background pixel& In computing and comparing the suitability of Otsu and bimodal histogram threshold extraction methods, VDVI, ExG and ExG-ExR display noticeable bimodal effects, while the histogram bimodal effect of NGRDI and GLI is not prominent, and the calculation result is still a qualitative description. The maximum class variance algorithm can be used to quantitatively obtain the threshold of the target and the background. Comparing the recognition characteristics of the five indices, the number of pitaya fruit pixels were extracted by VDVI accounted for 13.94% of the whole study area, the recognition accuracy was 97.437%, the Kappa coefficient was 0.9607, the recognition of pitaya fruit was higher, and the resolution was also good. The accuracy of GLI and ExG-ExR is only 42.57% and 58.96%, respectively. Clearly, the two indices misclassify other features or background values as target features. The target pixel scale of NGRDI is only 6.61%, and most of the pitaya fruit area is missed. ExG has evident recognition and extraction ability compared to ExG-ExR, NGRDI, and GLI, but there is still a certain missed phenomenon compared to VDVI. Combined with visual space modeling tool Model Builder, proposed a method of slicing the plant clusters by the average area of a single plant. Pitaya fruit of typical economic crops was used as the research objects for conducting plant recognition and information extraction methodology research. In conclusion: 1) Compared to EXG, NGRDI, ExG-ExR, and GLI vegetation indices, VDVI identified 161 species of dragon fruit, which reached 50.31% of the total number of plants in the study area and is the best segmentation method for dragon fruit recognition;2) Compared to the five indexes, it could well segment the target and background objects when the VDVI segmentation threshold is 0.037;3) Through the verification of the actual number of plants, and the number of extracted plants measured in the human-machine interaction base, the overall precision of the single plant extraction of pitaya fruit was 91.7%. The results confirmed that it is feasible to use visible light images as the data source, the VDVI as the identification method, and for plant cluster segmentation to be applied in the identification of dragon fruit in karst plateau gorge areas.